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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer SEMIRATA 2015 Seminar Nasional Informatika (SEMNASIF) CESS (Journal of Computer Engineering, System and Science) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Informatika JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Teknik Informatika UNIKA Santo Thomas MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Query : Jurnal Sistem Informasi JOURNAL OF SCIENCE AND SOCIAL RESEARCH KOMPUTA : Jurnal Ilmiah Komputer dan Informatika CSRID (Computer Science Research and Its Development Journal) Jurnal Varian JTIK (Jurnal Teknik Informatika Kaputama) KAKIFIKOM : Kumpulan Artikel Karya Ilmiah Fakultas Ilmu Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Teknik Informatika C.I.T. Medicom JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JUKI : Jurnal Komputer dan Informatika MEANS (Media Informasi Analisa dan Sistem) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Jurnal Ipteks Terapan : research of applied science and education Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Dinamika Informatika (JDI) Data Sciences Indonesia (DSI) International Journal of Economic, Technology and Social Sciences (Injects) Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Proceeding Of International Conference On Education, Society And Humanity Jurnal Teknik Indonesia (JU-TI) "Journal of Data Science
Zakarias Situmorang
FIKOM, Universitas Katolik Santo Thomas
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Forecasting Analysis of New Student Candidate Admissions Using the Simple Linear Regression Method Sardo Pardingotan Sipayung; Zakarias Situmorang; Zekson Matondang; Masdiana Sagala
Jurnal Teknik Indonesia Vol. 2 No. 02 (2023): Jurnal Teknik Indonesia (JU-TI), Desember 2023
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-ti.v2i02.561

Abstract

The development of science and technology facilitates various aspects of life, including forecasting. Forecasting student enrollment in private universities can maximize the use of resources for services, facilities, infrastructure, and improving human resources. The regression method is used to measure the effect of promotional costs on increasing student enrollment in the future. This forecasting will be valid if an accurate model is used. The results showed the level of accuracy using the MAPE (Mean Absolute Percentage Error) model of 2.229%. However, the level of accuracy can vary each due to differences in data.
Comparative Analysis of SVM and Perceptron Algorithms in Classification of Work Programs Jaka Tirta Samudra; Rika Rosnelly; Zakarias Situmorang
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 2 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i2.2479

Abstract

Government agencies are required to mobilize every aspect of publication which is carried out every year which must be accounted for and also carried out for each device that receives it such as assisted villages by utilizing available apbd funds in maximizing work programs designed so that they can be implemented optimally and effectively. by getting the best from all aspects of the work program implementation, of course there are important points in designing an annual work program without exception. data mining itself can help the department of population, family planning, women's empowerment and child protection in analyzing each work program design from before it is implemented onwards to look at various aspects of past data whose grouping is in the form of classification. The purpose of this study is to build a classification model with the addition of a sigmoid activation function that uses svm and perceptron to obtain a comparison value for the accuracy of the algorithm used to obtain the best working program design. The classification results are used to get the best value for classifying the best P2KBP3A work program dataset where it can be seen that the average accuracy value is 87.5%, the f1 value is 82.2%, the precision value is 80.2%, and the recall value is 87.5% so that the final result of the research results obtained a good accuracy value.
Long Short Term Memory and Gradient Boosting Model for One Day Ahead Forecasting of ANTAM Gold Bar Prices Ashari, Annisa; Situmorang, Zakarias; Rosnelly, Rika
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5630

Abstract

This study develops and optimizes a hybrid LSTM-XGBoost forecasting model for daily ANTAM gold bar prices. The model utilizes historical time-series data of ANTAM gold prices, enriched with macroeconomic variables including the USD/IDR exchange rate and Brent oil prices, as well as derived features such as returns, lags, rolling statistics, and calendar effects. The LSTM component captures medium-term sequential patterns from the price series and macroeconomic variables, while the XGBoost component exploits a rich set of tabular features to model nonlinear relationships and volatility dynamics. Both models are trained and tuned separately, then combined through a weighted ensemble scheme in which the optimal weight is selected by minimizing Mean Absolute Percentage Error (MAPE) on the validation set. Experimental results on the test set show that the proposed hybrid model achieves Mean Squared Error (MSE) of 26,891,172.36, Root Mean Squared Error (RMSE) of 16,398.53, MAPE of 0.0058 (approximately 99.42% accuracy), and coefficient of determination \mathbit{R}^\mathbf{2} of 0.9971, outperforming a naïve baseline that assumes “tomorrow’s price equals today’s price”. The optimized LSTM-XGBoost hybrid model proves highly effective for short-term ANTAM gold price forecasting, providing reliable decision support for Indonesian gold market stakeholders.
Co-Authors A F Limas Ptr Abwabul Jinan Adam Sagara Ade Clinton Sitepu Ade Clinton Sitepu Adelina, Mimi Chintya Aditia Rangga Alkhairi, Putrama Ashari, Annisa Asrizal Asrizal Asyahri Hadi Nasyuha B. Herawan Hayadi Budi K. Hutasuhut Daim Azhari Parinduri Desi Irfan Doughlas Pardede Efendi, Syahril Ela Roza Batubara Erna Budhiarti Nababan Fazli Nugraha Tambunan Ginting, Emnita Boru Handayani, Meli Hartono Hartono Herman Mawengkang Husein, Alice Erni Ichsan Firmansyah Irwan Daniel Irwan Daniel Ita Juwita Saragih Jaka Tirta Samudra Jaka Tirta Samudra Jaka Tirta Samudra Jaka Tirta Samudra Jazi Eko Istiyanto Junaidi Junaidi Karina Andriyani Kelvin Leonardi Kohsasih Khairul Fadhli Margolang Khoirunsyah Dalimunthe Kusuma, Jaka Lestari, Valencya Lewis, Andreas Lubis, Cindy Paramitha Lusi Herlina Siagian M Anggi Rivai Nst Manungkalit, Jupri Maria Claudia Purba Masri Wahyuni Mawaddah Harahap Mawaddah Harahap, Mawaddah Melda Pita Uli Sitompul Muhadi M. Ilyas Gultom Muhammad Mizan Siregar Muhammad Zarlis Muhammad Zarlis, Muhammad Nababan, Junerdi Novendra Adisaputra Sinaga Opim Salim Sitompul P.P.P.A.N.W. Fikrul Ilmi R.H. Zer Pradipta, Muhammad Iqbal Pratiwi, Mariska Putri Puji Sari Ramadhan Purba, Andry Hery Putrama Alkhairi Rahmad, Sofyan Retantyo Wardoyo Riandini, Maisarah Ridha Maya Faza Lubis Ridha Maya Faza Lubis Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly, Rika Rimbun Siringoringo, Rimbun Romanus Damanik Roslina, Roslina Rubianto Rubianto Sagala, Masdiana Sartika Mandasari Sembiring, Rahmat Widya Sipayung, Sardo Pardingotan Sri Hartati Sugeng Riyadi Tarigan, Dede Ardian Tulus Tulus Wanayumini Yoppi, Edunal Yuni Franciska Yusfrizal Yusfrizal Yusniar Lubis Yusniar Lubis Yusniar Lubis Zekson Matondang