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All Journal CogITo Smart Journal Jurnal Linguistik Komputasional Journal of Innovation and Future Technology (IFTECH) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Frontiers Jurnal Teknik Informatika (JUTIF) JOINTER : Journal of Informatics Engineering Journal La Multiapp GEOGRAPHIA: Jurnal Pendidikan dan Penelitian Geografi Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Ismart Edu: Jurnal Pendidikan Teknologi Informasi Jurnal Minfo Polgan (JMP) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Indonesia Sosial Teknologi Jurnal Penelitian Rumpun Ilmu Teknik Innovative: Journal Of Social Science Research Tadulako Science and Technology Journal Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Vivi Peggie Rantung
Universitas Negeri Manado
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Other

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Prediksi Jumlah Angkatan Kerja di Sulawesi Utara Menggunakan Algoritma Machine Learning Glenn Tyovanny Jeremy Karu; Vivi Peggie Rantung
Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 6 No. 1 (2026): EduTIK : Februari 2026
Publisher : Jurusan PTIK Universitas Negeri Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Prediksi jumlah angkatan kerja yang akurat merupakan kebutuhan penting bagi pemerintah daerah dalam menyusun kebijakan ketenagakerjaan dan perencanaan pembangunan sumber daya manusia. Provinsi Sulawesi Utara menunjukkan dinamika pasar tenaga kerja yang cukup signifikan dalam dua dekade terakhir, yang dipengaruhi oleh pertumbuhan penduduk, perubahan struktur ekonomi, serta berbagai guncangan eksternal seperti perlambatan ekonomi dan pandemi COVID-19. Kondisi tersebut menuntut adanya pendekatan prediktif yang mampu menangkap pola data yang kompleks dan tidak selalu linear. Penelitian ini menerapkan pendekatan machine learning untuk memprediksi jumlah angkatan kerja di Provinsi Sulawesi Utara menggunakan data tahunan periode 2003–2025. Data yang digunakan meliputi jumlah angkatan kerja, jumlah penduduk bekerja, tingkat pengangguran terbuka, dan total populasi. Tiga algoritma prediksi, yaitu Linear Regression, Random Forest, dan Extreme Gradient Boosting (XGBoost), diimplementasikan dan dibandingkan untuk menentukan model terbaik. Evaluasi performa model dilakukan menggunakan metrik Mean Absolute Error (MAE), Root Mean Square Error (RMSE), dan koefisien determinasi (R²). Hasil penelitian menunjukkan bahwa Linear Regression menghasilkan tingkat akurasi yang sangat tinggi namun mengindikasikan overfitting. Random Forest mampu menangkap hubungan non-linear, tetapi menunjukkan kestabilan yang terbatas akibat ukuran data yang relatif kecil. XGBoost memberikan performa paling seimbang dengan kesalahan prediksi yang stabil serta kemampuan generalisasi yang lebih baik. Berdasarkan model XGBoost terpilih, prediksi jumlah angkatan kerja periode 2026–2035 menunjukkan tren peningkatan yang konsisten. Temuan ini menunjukkan bahwa machine learning, khususnya XGBoost, efektif digunakan untuk peramalan ketenagakerjaan daerah.
Comparison of LSTM and ARIMA Methods in Predicting the Inflation Rate in Manado City Skolastika Kadang; Vivi Peggie Rantung
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1929

Abstract

Forecasting city-level inflation is challenging due to seasonal patterns, nonlinear dynamics, and limited exogenous variables, while short-term accuracy is required for timely policy responses. This study focuses on monthly inflation in Manado City over the period 2010–2024, explicitly accounting for the role of the Consumer Price Index (CPI). We compare a seasonal SARIMA baseline with a multivariate LSTM model that jointly ingests inflation and CPI series. The contributions of this work are an end-to-end, reproducible forecasting pipeline and an evidence-based comparison that identifies the conditions under which a feature-rich nonlinear model is preferable. The methodology includes aligning and preprocessing monthly series, conducting stationarity tests, selecting SARIMA specifications via information criteria and residual diagnostics, and training a 12-month window LSTM (Adam optimizer, MSE loss) with internal validation. The results show that the LSTM yields lower errors on the test horizon (RMSE 0.497; MAE 0.398) than the SARIMA (1,1,1)×(1,1,1,12) model (RMSE 0.661; MAE 0.486), with a smoother 12-month-ahead forecast path under a constant-CPI scenario; visual findings are consistent with the metrics, and a Diebold–Mariano test can be used to assess the significance of the difference. In conclusion, although SARIMA remains a strong and interpretable baseline, the multivariate LSTM delivers a practically meaningful gain in short-term accuracy when the inflation–CPI interaction is nonlinear, making it relevant for regional policy planning.
Implementation K-Means Algorithm in Promotional Media Destination Tour Minahasa Web Based Efraim Moningkey; Vivi Peggie Rantung; Peliks Andreas Surbakti
Journal La Multiapp Vol. 7 No. 1 (2026): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v7i1.2659

Abstract

Minahasa Regency has great tourism potential with a variety of destinations including cultural, natural, and man-made tourism. However, tourism promotion efforts still face obstacles due to the lack of integrated media capable of grouping destination information based on tourist interests and preferences. This study aims to apply the K-Means clustering algorithm in web-based promotional media to group Minahasa tourist destinations based on the level of user interaction, which is represented by the number of likes and comments on promotional content for each destination. The research method is carried out through several stages, namely collecting tourist destination data, pre-processing interaction data, implementing the K-Means algorithm with a specified number of clusters of three according to the main categories of tourismcultural, natural, and man-made), and implementing the clustering results into a web-based evaluation system that uses the Silhouette Coefficient to evaluate the quality of cluster formation. The results show that the K-Means algorithm is able to effectively group tourist destinations into three clusters that reflect the level of popularity, making it easier for users to find destination recommendations according to their interests. Implementation in a web-based system also provides an interactive display in the form of a list of destinations per cluster and recommendations for popular destinations. Thus, this study proves that the application of K-Means can increase the effectiveness of Minahasa tourism promotion, and in the future it can be developed with the integration of real-time data from social media and comparison with other clustering algorithms.
Co-Authors Abimanyu Marvie Dwisuprapto Andrew C. J. Mangkey Andrew Christensen Jehezkiel Mangkey Ayu Triana Situmorang Ayu Triana Situmorang Christiano Febriano Allesandro Franko Polii Christofel Owen Tendean Chrysilia Rimbing Cindy Pamela C Munaiseche Civita Loho Conggresco, Sherly Dalle, Asnir Daniel Riano Kaparang Dodu , Albertch Yordanus Erwin Efraim Moningkey Engelina Ester Pangaila Enjelina Enjelina Ezra Matthew Warouw Runturamby Ferdinan I. Sangkop Gabriel Tamboto Gideon Febri Tuuk Gladly Caren Rorimpandey Glen D. P. Maramis Glendy Koleangan Glenn Tyovanny Jeremy Karu Hajra Rasmita Ngemba Hiskia Kamang Manggopa Inda, Inda Joshua Johanes Natanael Pangaila Kakahis, Frimer Karamoy, Beauty Leony Kristofel Santa Lintine, Gabriella Bamba Ratih Lipan, Kezia Lisa Menden Luckie Sojow Mandas, Laura Rebeca Mangkey, Andrew Christensen Jehezkiel Marcelliano Riccardy Anantho Omega Kalitouw Mario Rettob Rettob Mario Tulenan Parinsi Medea, Mega Jayanti Meyly Olivia Worang Michella Undap Nancy Sylvia Bawiling Nanda, Agus Estepen Olivia Kembuan P. Dominggo, Nenita Pandoh, Kevin Mclaren parabelem tinno dolf rompas Peliks Andreas Surbakti Penidas Fodinggo Tanaem Putra Ramadhan, Adjie Quido Conferti Kainde Ramba, Rima Ramdan Adjis Ratumbuisang, Yosua Fitsgerald Rosmala Nur Shania Kaparang Simbala, Akmal Skolastika Kadang Sondy C. Kumajas Stevanus Alfius Falentino Kembuan Supit, Mesiasi Anjelika Syaiful Hendra Tambajong, Karmel Daud Trudi Komansilan Wongkar, Abdiel Jeremia Winston Worang, Meyly Olivia Wowor, Hanna Elisabeth Wuntu, Lucky