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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Palapa: Jurnal Study Keislaman dan Ilmu Pendidikan BAREKENG: Jurnal Ilmu Matematika dan Terapan AS-SABIQUN: Jurnal Pendidikan Islam Anak Usia Dini Nusantara Science and Technology Proceedings Journal of Applied Data Sciences SINTA Journal (Science, Technology, and Agricultural) Journal of Informatics Management and Information Technology Komatika: Jurnal Pengabdian Kepada Masyarakat Sungkai Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) ALSYS : Jurnal Keislaman dan Ilmu Pendidikan ANWARUL: Jurnal Pendidikan dan Dakwah MASALIQ: Jurnal Pendidikan dan Sains Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Proceeding of International Conference on Humanity Education and Society Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Yuliana Susanti, Yuliana
Program Studi Agroteknologi
Religion Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Medicine & Pharmacology Physics Social Sciences Transportation Veterinary Other

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IMPLEMENTATION OF CROSS-VALIDATION ON HANG SENG INDEX FORECASTING USING HOLT’S EXPONENTIAL SMOOTHING AND AUTO-ARIMA METHOD Sucipto, Christy Sheldy; Sulandari, Winita; Susanti, Yuliana
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp13-24

Abstract

This study applies a rolling window cross-validation to evaluate the multi-step forecasts instead of using the traditional single split for Hang Sheng Index (HSI) forecasting. The forecasting methods discussed in this study are Holt's Exponential Smoothing and auto ARIMA, chosen because of their ability to model trend data as in the daily HSI. This research aims to evaluate up to five step forecast values obtained by the two forecasting methods built in the training data with rolling window cross-validation. In the experiment, each of the 21 auto ARIMA and Holt's models was constructed from 84 observations (as in-sample data) obtained from the rolling window cross-validation. The one to five step forecast values of daily HSI are then calculated using those models, and the accuracy of each forecast value is evaluated based on Mean Absolute Percentage Error (MAPE). The results show that the Auto ARIMA model produces a lower MAPE value than Holt's model, namely 2.9196%, 4.6553%, 6.4012%, 8.3083%, and 10.3781%, respectively, for one to five steps ahead. Therefore, auto ARIMA is more recommended for forecasting HSI values up to five steps ahead than Holt's method.
TRUNCATED SPLINE SEMIPARAMETRIC REGRESSION TO HANDLE MIXED PATTERN DATA IN MODELING THE RICE PRODUCTION IN EAST JAVA PROVINCE Handajani, Sri Sulistijowati; Pratiwi, Hasih; Respatiwulan, Respatiwulan; Susanti, Yuliana; Nirwana, Muhammad Bayu; Nareswari, Lintang Pramesti
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2597-2608

Abstract

Climate change can affect rice production through changes in temperature, precipitation patterns, extreme weather events, and atmospheric carbon dioxide levels. A statistical model can be used to understand the correlation between rice production and factors that affect it. The existence of some patterns that are formed from independent variables and others that do not show data patterns due to volatility in weather element data makes semiparametric regression modeling more appropriate. In forming a parametric model, the data pattern needs to be regular to make the model more precise. Irregular data patterns are more appropriately modeled with nonparametric regression models. The existence of several patterns formed from independent variables to their dependent variables, and several others, does not show a particular pattern due to the volatility in climate data, making truncated spline semiparametric regression modeling more appropriate to use. This research aims to model rice production in several regions in East Java Province in 2022 using a semiparametric regression model. The data used were from the Meteorology, Climatology, and Geophysics Agency and the Central Statistics Agency for East Java Province in 2022. The response variable is the rice production (tons) in 2022 in Tuban, Gresik, Nganjuk, Malang, Banyuwangi, and Pasuruan Regency (Y). The predictor variables are paddy harvested area (hectares), average temperature (℃), humidity (percent), and rainfall (mm). The semi-parametric spline truncated regression model is obtained by combining the parametric and non-parametric models based on truncated splines. The analysis showed a spline truncated semiparametric regression model with a combination of knot points (3,3,1) with a minimum GCV value of 12,642,272. The variables significantly affecting rice production were rice harvest area, temperature, air humidity, and rainfall, with an adjusted value of 98.522%.
ANALISIS REGRESI ROBUST ESTIMASI GM PADA INDEKS KEPARAHAN KEMISKINAN PROVINSI-PROVINSI DI INDONESIA Aristiarto, Rio; Susanti, Yuliana; Susanto, Irwan
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 7, No 1 (2023): SEMNAS RISTEK 2023
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v7i1.6273

Abstract

Indeks keparahan kemiskinan merupakan indikator yang dapat digunakan untuk melihat perkembangan kemiskinan. Indeks ini memberikan gambaran mengenai penyebaran pengeluaran di antara penduduk miskin. Kemiskinan di Indonesia selama tiga tahun terakhir terjadi peningkatan. Penelitian ini bertujuan untuk mengetahui faktor-faktor yang mempengaruhi indeks keparahan kemiskinan provinsi-provinsi di Indonesia. Data indeks keparahan kemiskinan tahun 2021 mengandung pencilan di dalamnya sehingga asumsi normalitas tidak terpenuhi. Salah satu metode yang dapat digunakan dalam mengatasi pencilan yaitu analisis regresi robust. Estimasi yang digunakan adalah Generalized M (GM) yang merupakan pengembangan dari estimasi M ketika estimasi M kurang sensitif terhadap pencilan. Hasil penelitian menunjukkan bahwa faktor-faktor yang berpengaruh signifikan terhadap indeks keparahan kemiskinan provinsi-provinsi di Indonesia tahun 2021 adalah persentase penduduk miskin, indeks pembangunan manusia, dan proporsi rumah tangga dengan status rumah milik sendiri.
PERAMALAN HARGA SAHAM PT UNILEVER INDONESIA MENGGUNAKAN METODE HIBRIDA ARIMA-NEURAL NETWORK Setiawan, Crisma Devika; Sulandari, Winita; Susanti, Yuliana
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 7, No 1 (2023): SEMNAS RISTEK 2023
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v7i1.6270

Abstract

Saham merupakan salah satu instrumen investasi yang diminati oleh banyak investor dan memiliki tingkat keuntungan yang menarik. Saham dari PT Unilever merupakan salah satu saham yang aktif diperjual belikan dalam BEI dan tergabung dalam LQ45. Kinerja perusahaan ditunjukkan melalui harga saham dari perusahaan tersebut dan para investor perlu memprediksi harga sebuah saham untuk mengurangi resiko kerugian. Harga saham yang selalu berfluktuasi memungkinkan data historisnya memiliki hubungan linier dan nonlinier. Penelitian ini menggunakan metode hibrida ARIMA – Neural Network untuk memprediksi harga saham PT Unilever periode Januari hingga Desember 2019, karena metode ini digunakan untuk memprediksi runtun waktu yang linier maupun non linier. Hasil akhir penelitian ini menunjukkan bahwa model ARIMA terbaik adalah ARIMA (3,1,2) dengan nilai MAPE data latih 1.04% dan data uji 0.86%, sedangkan model hibrida terbaik adalah ARIMA (3,1,2) – NN (4,9,1) dengan nilai MAPE data latih dan data uji berturut adalah 1,03% dan 0,82%. Model hibrida memiliki nilai MAPE lebih kecil dibandingkan model ARIMA, tetapi tidak memberikan perbedaan hasil peramalan yang signifikan. Meskipun demikian model hibrida dapat menambah tingkat keakuratan peramalan pada harga saham unilever.
Uji Kompatibilitas Bakteri Endofit Asal Tanaman Eucalyptus pellita dan Fungi Mikoriza Arbuskular (FMA) Susanti, Yuliana
SINTA Journal (Science, Technology, and Agricultural) Vol. 3 No. 2 (2022)
Publisher : Perkumpulan Dosen Muda (PDM) Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37638/sinta.3.2.111-120

Abstract

Penyakit layu bakteri (PLB) yang disebabkan oleh Ralstonia solanacearum telah menjadi masalah besar dalam silvikultur eukaliptus pada hutan tanaman industri (HTI). Penyakit ini membatasi produktivitas tanaman eukaliptus. Upaya-upaya pengendalian telah dilakukan, salah satunya adalah penggunaan bakteri endofit. Aplikasi bakteri endofit secara tunggal menunjukkan hasil yang belum memuaskan. Kombinasi bakteri endofit dan fungi mikoriza arbuskular (FMA) merupakan alternatif pengendalian penyakit layu bakteri pada tanaman eukaliptus yang belum dilaporkan. Penelitian ini bertujuan untuk memperoleh kombinasi bakteri endofit dan FMA yang kompatibel pada tanaman Eucalyptus pellita. Metode penelitian meliputi penyiapan bakteri endofit, penyediaan dan perbanyakan FMA, serta penyediaan bibit E. pellita. Hasil penelitian diperoleh kombinasi bakteri endofit dan FMA yang kompatibel. Interaksi kedua mikrob dapat meningkatkan pertumbuhan bibit tanaman E. pellita.
UJI ANTAGONISME CENDAWAN Trichoderma sp TERHADAP Ganoderma Boninense (PATOGEN PADA TANAMAN KELAPA SAWIT) SECARA IN VITRO Putra, Sona Syah; Susanti, Yuliana; Alfiah, Lufita Nur
SINTA Journal (Science, Technology, and Agricultural) Vol. 5 No. 1 (2024)
Publisher : Perkumpulan Dosen Muda (PDM) Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37638/sinta.5.1.125-134

Abstract

Stem Root Rot (BPB) is a disease caused by the fungus Ganoderma Boninense. BPB results in low production of oil palm plants. Biological control using the fungus Trichoderma sp is an alternative that is currently being widely researched to control plant diseases. This research aims to determine the potential inhibitory ability of Trichoderma sp on the growth of G. Boninense in vitro. The research method used was double culture with isolates of Trichoderma Asperellum (T1), Trichoderma Asperellum (T2), Trichoderma Harzianum (T3) against G. Boninense. The research results showed that Trichoderma Asperellum (T1) had an inhibitory power of 72.3%, Trichoderma Harzianum (T3) had an inhibitory power of 72.2% and Trichoderma Asperellum (T2) had the highest antagonistic power reaching 92.5%. the three isolates of antagonistic fungi can inhibit the fungus G. Boninense
Co-Authors Alfiah, Lufita Nur Andreas Rony Wijaya Aristiarto, Rio Arnandya, Evelyn Regita Astuti, Arinda Tri Brilianto, Maximilianus Noel Edward Bahar Elwi Handajani, Sri Sulistijowati Handayani, Sri Sulistijowati Hilyatul Hilwy, Sarah Irwan Susanto Irwan Susanto Isnandar Slamet, Isnandar Kiki Ferawati Kusuma, Erin Jihan Wahyu Mahmudah, Arik Manalu, Anggiat Nareswari, Lintang Pramesti Nirwana, Muhammad Bayu Nugrahani , Isnaini Dyah Nurfauziyyah, Nuha Pratama, Alfito Putra Fajar pratiwi, hasih Putra, Sona Syah Qona’ah, Niswatul Raditya, Dionisius Indra Ratri, Aninda Puspa Respatiwulan, Respatiwulan Rina Febrinova, Rina Setiawan, Crisma Devika Sri Subanti Sucipto, Christy Sheldy Sugiyanto - Susilotomoa, Dhestahendra Citra Triyazulfa, Azkiya Winita Sulandari Zukhronah, Etik