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All Journal Jurnal Pendidikan Matematika dan IPA Journal of Tropical Soils Jurnal Mahasiswa Ilmu Pemerintahan Universa Medicina Industria: Jurnal Teknologi dan Manajemen Agroindustri Pendas : Jurnah Ilmiah Pendidikan Dasar Unes Law Review Agroteknika Variance : Journal of Statistics and Its Applications Community Development Journal: Jurnal Pengabdian Masyarakat Law Research Review Quarterly REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat LANGUAGE: Jurnal Inovasi Pendidikan Bahasa dan Sastra Journal of Mathematics: Theory and Applications Salam (Islamic Economics Journal) Technologica Tsaqofah: Jurnal Penelitian Guru Indonesia Electronic Journal of Education, Social Economics and Technology Jurnal Pendidikan dan Pengabdian Masyarakat COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Jurnal Ilmu Manajemen Dan Kewirausahaan (JIMK) IJRETINA - International Journal of Retina atech-i Parameter: Jurnal Matematika, Statistika dan Terapannya Communale Journal PENGAMATAN: Jurnal Pengabdian Masyarakat untuk Ilmu MIPA dan Terapannya
Yudistira Yudistira
STMIK AMIKOM Purwokerto
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Journal : Variance : Journal of Statistics and Its Applications

 1 
PERFORMANCE ANALYSIS OF RANDOM FOREST CLASSIFICATION ON UNEMPLOYMENT RATE IN MALUKU PROVINCE BASED ON DATA BALANCING METHOD Yunizar, Mahdayani Putri; Sinay, Lexy Janzen; Yudistira, Yudistira
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 1 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss1page31-38

Abstract

In 2023, the number of unemployed people in Maluku will reach 59,800 or 6.08% of the total population. To reduce unemployment in Maluku, it is essential to understand the unemployment situation of the Moluccan population based on socioeconomic factors immediately. Therefore, applying classification methods such as random forests is the right step, but it is recommended that the data be balanced to get accurate results. However, the unemployment rate in Maluku is much lower than that of the unemployed, so data imbalance affects the accuracy of the classification results. Therefore, a data balancing process is needed, among others, using the Random Oversampling of Sample (ROSE), Synthetic Minority Oversampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN) methods. This study uses data from the 2023 National Labor Force Survey (SAKERNAS) conducted in February by the Central Statistics Agency (BPS) of Maluku. The number of unemployed people is smaller than the number of unemployed residents. Therefore, action needs to be taken to address data inequality. The results of this study show that the random forest classification model with SMOTE has the best performance with a combination of 90% training data and 10% testing data, with a higher AUC value than other methods, and age variables are the most essential variables built into the model.
MARKOV CHAIN ANALYSIS FOR PREDICTION OF MONTHLY AVERAGE TEMPERATURE PATTERNS AT PATTIMURA METEOROLOGICAL STATION AMBON 2015 - 2024 Selangur, Djudid Sintje; Lestaluhu, Musfa Rizaldi; Qadry, Alwatia Al; Huwae, Angel Gressovin; Toumahuw, Imanuella; Lewen, Dorothy H.; Seknun, Fitri R.; Namkatu, Jalianti; Yudistira, Yudistira
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 2 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss2page199-208

Abstract

Weather has a significant impact on human activities, making accurate weather forecasting an important necessity. This study aims to analyze the patterns of climate element changes at the Pattimura Ambon Meteorological Station using the Markov Chain approach to identify climate transition patterns, estimate steady-state time, and predict the climate in 2025. Monthly secondary climate element data for the period 2015-2024 were obtained from the Maluku Province BPS, categorized into three conditions: cold (<25°C), normal (25°C-26.5°C), and hot (>26.5°C). The data were analyzed using the Markov Chain method with calculations of the transition probability matrix, matrix convergence, and steady-state distribution. The research results indicate that the system reaches equilibrium after 47 periods with a long-term distribution: cold condition 2.85%, normal 35.66%, and hot 61.76%. The hot condition has the highest stability with a probability of remaining in the same state at 91.8%. The 2025 prediction indicates that monthly temperature probabilities gradually move toward the steady-state distribution, illustrating the dominance and persistence of hot conditions in the long term. The analysis results provide important implications for agricultural planning, tourism, infrastructure, and disaster mitigation in the city of Ambon in the face of climate change.
Co-Authors Abdullah, Salma Adnan, Muh. Akbar, Fauzia Alfian Allen, Rio Valery Ampauleng, Ampauleng Anindya, Anindya Ansory, Ahmad Anto, Ahmad Apriyanti, Maulita Dwi Ardani, Farell Ari Andayani Ayuningsih, Fitria Balami, A. M. Batubara, Fanny Yuliana Cupian Cupian da Costa, Julieta Darman, Luky Prassetya Deded Chandra Defrian, Angga Dino Caesaron Djinis, Musdar Effy Dussy, Achmad Nur Azhary Effy Djinis, Musdar Effy Djinis Ernita, Yuni Fathur Rizal Ferry Kondo Lembang H Hasmawati Hakim, Irfan Halim, Ardiansyah Hasibuan, Melkisedek Hasman, Elvin Hasyim, Fuadz Hatala, Devita Haumahu, G. Hendrawan, Kevin Anggakusuma Herdian, Fithra Husnah, Maulida Huwae, Angel Gressovin Ilma Mufidah Irma Setiawan, Irma J. Djami, Ronald J. Wattimanela, Henry Jusmaldi Jusmaldi Kilikily, Aditia Rajasa Kifta L, Rosanti Laamena, Novita S. Laamena, Novita Serly Laode Muhammad Harjoni Kilowasid Latupeirissa, S. J. Lembang, F. K. Lestaluhu, Fitri Lestaluhu, Musfa Rizaldi Lewaherilla, Norisca Lewen, Dorothy H. Lexy Janzen Sinay, Lexy Janzen Loklomin, S. B Loklomin, S. B. Lusye Bakarbessy Mahubessy, Juan Felix Benicktus Malik, Kamil Malrianti, Yefsi Manalu, Omena Maswahenu, Mara Matdoan, M. Y. Ma’bar, M. Fadin Medi Hendra, Medi Muhammadin, Akhmad Musa Namkatu, Jalianti Nanlohy, Y. W. A. Ni Made Ari Suryathi Ningsi, Fitri Wahyu Noven, Sarah Annisa Nurinayah, Sevi Palisoa, N. F. Qadry, Alwatia Al Rahma, Ayu Dyah Rahmaniar, Wa Ode Rifdian, Filandia Rosalina Salhuteru S Laamena, Novita Safitri, Yuniar Andini Sedubun, Debora Ribka Seknun, Fitri R. Seknun, M. F. Selangur, Djudid Sintje Sinay, L. J. Sinurat, Frisella Br Siska Siska Sjahruddin, Herman Sri Aulia Novita Sudirman Sudirman Syifa, Anisa Tamrin Tamrin Toumahuw, Imanuella Umbu Lapu, Ervan Suryanti Y. Matdoan, Muhammad Yunizar, Mahdayani Putri Zulnadi, Zulnadi