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All Journal Semantik TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Pro Bisnis Telematika JUITA : Jurnal Informatika Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan ILKOM Jurnal Ilmiah Jurnal Tekno Insentif JURTEKSI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Terpadu Building of Informatics, Technology and Science Journal of Information Systems and Informatics Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia Journal La Multiapp JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Algoritma Jurnal Informatika: Jurnal Pengembangan IT Journal of Informatics and Interactive Technology (JIITE) INJECT Interdisciplinary Journal of Communication Journal of Digital Market and Digital Currency
Fandy Setyo Utomo
Universitas AMIKOM Purwokerto
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Rectified Linear Units and Adaptive Moment Estimation Optimizer on ANN with Saved Model Prediction to Improve The Stock Price Prediction Framework Performance Sekhudin, Sekhudin; Purwati, Yuli; Utomo, Fandy Setyo; Azmi, Mohd Sanusi; Subarkah, Pungkas
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1586.271-282

Abstract

A stock is a high-risk, high-return investment product. Prediction is one way to minimize risk by estimating future prices based on past data. There are limitations to solving the stock prediction problem from previous research: limited stock data, practical aspects of application, and less than optimal stock price prediction results. The main objective of this study is to improve the prediction performance by formulating and developing the stock price prediction framework. Furthermore, the research provides a stock price prediction framework that can produce better prediction results than the previous study with fast computation time. The proposed framework deals with data generation, pre-processing and model prediction. In further, the proposed framework includes two prediction methods for predicting stock closing prices: stored model prediction and current model prediction. This study uses an artificial neural network with Rectified Linear Units as an activation function and Adam Optimizer to predict stock prices. The model we have built for each forecasting method shows a better MAPE value than the model in previous studies. Previous research showed that the lowest MAPE was 1.38% for TLKM shares and 0.81% for BBRI. Our proposed framework based on the stored model prediction method shows a MAPE value of 0.67% for TLKM shares and 0.42% for BBRI. While the current model prediction method shows a MAPE value of 0.69% for TLKM shares and 0.89% for BBRI. Furthermore, the stored model prediction method takes 1.0 seconds to process a single prediction request, while the current model prediction takes 220 seconds.
A Comprehensive Evaluation of CatBoost and LightGBM Algorithms for Honorarium Prediction on Categorical Datasets with Class Imbalance Slamet Widodo; Fandy Setyo Utomo; Berlilana
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 3, November 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i3.27363

Abstract

Determining income, including honoraria in the academic environment, is often done manually and subjectively, necessitating a predictive model to objectively determine the honorarium amount. However, the development of the prediction model faces challenges due to the dataset's characteristics, which include categorical data and an imbalanced class distribution. This research aims to evaluate the predictive performance and computational resource efficiency of the CatBoost and LightGBM algorithms in predicting honorariums. The dataset used includes 58,332 actual honorarium data of employees from higher education institution "A" in Purwokerto for the period from January 2024 to February 2025. The methods used include data preprocessing, dataset splitting using Stratified Splitting, modeling with CatBoost, LightGBM, Random Forest, Neural Network, and Linear Regression, as well as evaluation using MSE, RMSE, MAE, R² metrics, and computational resources (execution time, memory, CPU time). LightGBM achieved an RMSE of 665.960 and an R² of 0.54, while recording the lowest memory usage at only 2.67 MB. CatBoost produced an RMSE of 667.395 and an R² of 0.53, excelling in processing categorical features without one-hot encoding. Meanwhile, Linear Regression showed the lowest accuracy and high memory usage. These results confirm that LightGBM is the most optimal choice for fast, efficient, and accurate honorarium predictions. However, this research is limited to testing in a laboratory environment. Further research is recommended to implement direct integration with an active database and the integration of information retrieval methods to enhance the effectiveness and security of real-time honorarium predictions, as well as to integrate interpretability methods such as SHAP to improve decision-making transparency.
Co-Authors Adiya, Az Zahra Dwi Nur Afit Ajis Solihin Aisha Hukama Setyowati Aji Saeful Aji Septa, Adrian Ajis Solihin, Afit Amar Al Farizi Anas Nur Khafid Anggini, Melisa Anggraeni, Mutia Dwi Anggraini, Nova Anggriani, Epri Anies Indah Hariyanti Azhari Shouni Barkah Azmi, Mohd Sanusi Bagus Adhi Kusuma Bahari, Aris Ridky Setiya Bahari, Aris Rifki Setiya Baihaqi, Wiga Maulana Balit, Muhamad Naufal Burhanuddin Berlilana Berlilana Berlilana Burhanuddin Balit, Muhamad Naufal Churil Aeni, Agustina Chyntia Raras Ajeng Widiawati Chyntia Raras Ajeng Widiawati Darmono Dedi Purwanto, Dedi Didi Prasetyo Dwi Krisbiantoro, Dwi Dwi Putriana Nuramanah Kinding Dzaky Candy Fahrezy Fadhilah, Siti Nur Faradina Faradina Faradina, Faradina Febriansyah Husni Adiatma Giat Karyono Giat Karyono Had, Iqbaluddin Syam Hanif Hidayatulloh Hendra Marcos, Hendra hidayatulloh, hanif Ilham, Rifqi Arifin Imam Tahyudin Indriyani, Ria Jamie Mayliana Alyza Kafilla, Princess Iqlima Kusuma, Bagus Adhi Kusuma, Velizha Sandy Lasmedi Afuan Latif, Ahmad Lubna, Zuhriyatul Lukita, Dita Maulana Baihaqi, Wiga Mohd Fairuz Iskandar Othman Mohd Nazrin Muhammad Mohd Sanusi Azmi Muaziz, Imam Muhamad Naufal Burhanuddin Balit Muhtyas Yugi Murtiyoso Murtiyoso Nandang Hermanto Nanna Suryana Nikmah Trinarsih Nugroho, Khabib Adi Nugroho, Lustiyono Prasetyo Nur Cholis Romadhon Octavia, Annisa Suci Prayoga, Fandhi Dhuga Pungkas Subarkah Purbo, Yevi Septiray Purwidiantoro, Moch. Hari Pyawai, Hero Galuh R. Vitto Mahendra Putranto Ramadhan, Aziz Ramadhan, Rio Fadly Rifqi Arifin Ilham RR. Ella Evrita Hestiandari Rujianto Eko Saputro Sagita, Selvi Samsul Arifin Sarmini - Sarmini Sarmini Sarmini Sekhudin, Sekhudin Setiabudi, Rizki Setiawan, Ito Shafira, Lulu Shendy Filanzi Slamet Widodo Slamet Widodo Sofa, Nur Sri Hartini Sugianto, Dwi Suryana, Nanna Taqwa Hariguna Taqwa Hariguna Titi Safitri Maharani Trinarsih, Nikmah Turino, Turino Utomo, Dadang Wahyu Wahid, Arif Mu'amar Wanti, Linda Perdana Wibisono, Arif Cahyo Wiga Maulana Baihaqi Yugi, Muhtyas Yuli Purwat Yuli Purwati Yuli Purwati Yulianto, Koko Edy