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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Teknik Informatika UNIKA Santo Thomas JUKI : Jurnal Komputer dan Informatika MEANS (Media Informasi Analisa dan Sistem) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Geosfera: Jurnal Penelitian Geografi Majalah Ilmiah METHODA TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Jurnal Widya Climago Jurnal Pengabdian Masyarakat Nauli
Marzuki, Sinambela
Badan Meteorologi, Klimatologi dan Geofisika
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika UNIKA Santo Thomas

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Classification of Labor Using Support Vector Machine in North Sumatera Ritonga, Anggiat P; Adithya, Andri Ramadhan; Agustina, Idri; Limbong, Tonni; Sinambela, Marzuki
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 4 No 2: Tahun 2019
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (610.001 KB) | DOI: 10.17605/jti.v4i2.658

Abstract

Labor markets in Indonesia are key challenges and policy issues. Balai Besar Pengembangan Latihan Kerja (BBPLK) Medan is a services unit to develop and implementation of labor to increase skill and knowledge. The classification of labor in North Sumatera is very interesting to evaluate the performance of the labor in North Sumatera. In this case, we compute the labor data to classify and evaluate the model and performance of the dataset. The computation of the dataset using the support vector machine (SVM) as a model in machine learning or probabilistic approach by training and test data. The data was collected from Badan Pusat Statistik (BPS) Sumatera Utara for 2018 samples. Labor force dataset in North Sumatera had been computed and shown the result, indicates the support vector machine classifier is the good algorithm for this classification problem, offering good values in terms of accuracy, for describe the labor force in North Sumatera and can be recommended to BBPLK to add more development and implementation.
A Web-Based Machine Learning Approach for Standardized Precipitation Index Prediction Hadi, Ahmad Fauzi Faishal; Sinambela, Marzuki; Rachmawardani, Agustina; Trihadi, Edward
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 10 No. 1 : Tahun 2025
Publisher : LPPM UNIKA Santo Thomas

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Abstract

Accurate and user-friendly drought forecasting tools are crucial for mitigating the impact of meteorological droughts, particularly in vulnerable areas such as South Sumatra, Indonesia. This study introduces an interactive web-based application built to anticipate drought conditions by forecasting the Standardized Precipitation Index (SPI). The system relies on deep learning techniques trained using three decades of rainfall data collected from the Climatological Station in South Sumatra. In evaluating model performance, both Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) architectures were assessed. While both models delivered comparable short-term predictions, the LSTM experienced a significant decline in accuracy over extended forecasting periods (specifically at SPI-6), primarily due to overfitting. In contrast, the RNN displayed more stable and reliable results, making it the preferable model for this geographical context. Specifically, the RNN achieved a lower Mean Absolute Error (MAE) of 0.4007, a reduced Root Mean Squared Error (RMSE) of 0.4684, and a higher coefficient of determination (R²) of 0.7338. These metrics outperformed those of the LSTM, which recorded a MAE of 0.4115, an RMSE of 0.4840, and an R² of 0.7036. Such results confirm that the RNN offers a more precise and dependable fit for the station’s dataset. The web platform also effectively visualizes the model outputs, providing a dynamic and interactive 24-month forecast view that supports early warning efforts and informed decision-making for regional authorities and stakeholders.
Co-Authors Adi, Suko Prayitno Adithya, Andri Ramadhan Agustina, Idri Albertus Tua Simanullang Alexander F.T Parera Bertalina Sihotang Darwis Robinson Manalu Dumayanti, Imelda Sri Dwiyanto . Emerson Limbong Eva Darnila Eva Darnila eva darnila, eva darnila FAUZAN, RAIHAN AHMAD Hadi, Ahmad Fauzi Faishal Hartoyo, Puji Indra Kelana Jaya Jamaluddin Jamaluddin Jamaluddin, Jamaluddin Janner Simarmata Larosa, FGN Larosa Manurung, Samuel Margono Mira Hestina Ginting Munawar Munawar Naibaho, Jimmy F. Niken Woro Palupi Nur Hidayat Patra Mesiari Tarigan Rachmawardani, Agustina Rahayu, Teguh RB. Yanuar Harry W Riama, Nelly Florida Ritonga, Anggiat P Rumahorbo, Benget RUSANTO, BENYAMIN HERYANTO Saragih, Naikson F. Setiawan, Elyas Silalahi, Veraci Simanullang, Harlen Sinaga, Winton Sudarisman, Maman SUHARINI, YUSTINA SRI Sunardi Sunardi Sunardi, Sunardi Supandi, Achmad Surianto Sitepu Teguh Rahayu Teguh Rahayu TONNI LIMBONG Trihadi, Edward Widada Sulistya Wisnu Wardono Yohanna, Margaretha Yosvin Apriando Yudha, I Putu Putra Wira Sarwa