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All Journal PROSIDING SEMINAR NASIONAL CENDEKIAWAN BAREKENG: Jurnal Ilmu Matematika dan Terapan Saintifik : Jurnal Matematika, Sains, dan Pembelajarannya Variance : Journal of Statistics and Its Applications Science Map Journal Tensor: Pure and Applied Mathematics Journal Jurnal Satya Informatika Sinergi: Jurnal Pengabdian Kepada Masyarakat Seminar Nasional Inovasi Teknologi Kocenin Jurnal Pengabdian Kepada Masyarakat Parameter: Jurnal Matematika, Statistika dan Terapannya BAKTI : Jurnal Pengabdian Kepada Masyarakat PENGAMATAN: Jurnal Pengabdian Masyarakat untuk Ilmu MIPA dan Terapannya
Laamena, Novita Serly
Program Studi Statistika, Universitas Pattimura
Religion Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Journal : Tensor: Pure and Applied Mathematics Journal

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Application of The Naïve Bayes Algorithm Method for Classification of Families at Risk of Stunting (Case Study: Waeapo District, Buru Regency) Noya Van Delsen, Marlon Stivo; Laamena, Novita Serly; Rumanama, Siti Adnan
Tensor: Pure and Applied Mathematics Journal Vol 5 No 2 (2024): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol5iss2pp111-118

Abstract

Classification is a job of assessing data objects to put them into a certain class from a number of available classes. One algorithm that can be used for classification is the Naïve Bayes Classifier. Naïve Bayes Classifier is a probability concept that can be used to determine class groups of text documents and can process large amounts of data with high accuracy results. The aim of this research is to determine the results of the classification of families at risk of stunting in Waeapo District, Buru Regency and to determine the level of accuracy of three data proportions, namely 70:30, 80:20 and 90:10. The sample in this study was 2290 families. Based on the known level of accuracy, the best accuracy value is a data proportion of 90:10 with an accuracy value of 93.9%.
Forecasting Inflastion Rate In Ternate City Using ARIMA Method And ARIMAX Calender Variation Ilu, Riski Noviyanti; Djami, Ronald John; Laamena, Novita Serly
Tensor: Pure and Applied Mathematics Journal Vol 6 No 2 (2025): Vol 6 No 2 (2025): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol6iss2pp111-120

Abstract

Indonesia sebagai negara berkembang sering menghadapi tantangan inflasi, baik nasional maupun daerah, seperti Kota Ternate yang menjadi acuan inflasi di Maluku Utara. Penelitian ini bertujuan untuk meramalkan inflasi di Kota Ternate menggunakan metode ARIMA dan ARIMAX Variasi Kalender berdasarkan data inflasi bulanan tahun 2014–2023 yang diperoleh dari website resmi BPS. ARIMA menangkap pola historis, sedangkan ARIMAX variasi kalender mempertimbangkan faktor musiman dan hari raya. Hasil menunjukkan model ARIMA terbaik adalah ARIMA ([1,4,12,13],0,0) dengan AIC minimum sebesar -184,729 dan tingkat akurasi sebesar 96,71%. Sementara model ARIMAX dengan variabel variasi kalender terbaik adalah ARIMAX ([13],0,[3]) dengan dummy m1 sampai m11 (bulan musiman) dan h2 (bulan hari raya), menghasilkan tingkat akurasi sebesar 97,16%. Dengan demikian, ARIMAX dengan variabel variasi kalender memberikan hasil peramalan yang lebih akurat dan relevan untuk mendukung kebijakan pengendalian inflasi daerah.
Co-Authors Bosar Panjaitan Bosar Panjaitan Bustan, Ariestha W Bustan, Ariestha Widyastuty Bustan, Ariestha Widyastuty Faisal Zuli Henry Junus Wattimanela Hiariey, Arlene H Ilu, Riski Noviyanti Istiqomah Sumadikarta Latupeirissa, Indri Kezia Lusye Bakarbessy Mayawi Mayawi Munazat Salmin Noya Van Delsen, M.S. Noya Van Delsen, Marlon Stivo Nurhayati Nurhayati Nurhayati Nurhayati Ronald John Djami Ronald John Djami Rosalina Salhuteru Rumanama, Siti Adnan Salhuteru, R. Sanlly Joanne Latupeirissa Saputra, Lamanisa Rasid Sedubun, Debora Ribka Taufan Talib Teguh Budi Santoso Wattimanela, H. J. Yonlib Weldri Arnold Nanlohy Yudistira Yudistira Yusriani Sapta Dewi