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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 Prosiding University Research Colloquium MASALIQ: Jurnal Pendidikan dan Sains Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Jurnal Indonesia Sosial Teknologi Proceeding of International Conference on Humanity Education and Society Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Yuliana Susanti, Yuliana
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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
Robust Regression Generalized Scale (GS) Estimation On Profit Data Of Poultry Farm Companies Callisa, Safira; Susanti, Yuliana; Susanto, Irwan
Prosiding University Research Colloquium Proceeding of The 15th University Research Colloquium 2022: Bidang MIPA dan Kesehatan
Publisher : Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

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

Abstract

Poultry farming is the business of cultivating poultry such as breeding chickens, laying hens, and broilers to obtain meat and eggs. Robust regression is a regression method that is used when some outlier data affect the model so that the distribution of the error is not normal. Estimates on robust regression that can overcome outliers such as Generalized Scale (GS) estimation, GS estimation is seen as an extension of S estimation. GS estimation is a solution for minimizing M estimation with paired scale error. This estimate is applied to poultry data companies in 2020 as an indicator to determine the robust regression model. It is concluded that the factors that affect the total profit of poultry farming companies in Indonesia in 2020 are wages for workers and electricity and water.
Parameter Estimation Robust Regression Method of Moment (MM) in Cases of Maternal Death in Indonesia Pramesti, Putri Ayu; Susanti, Yuliana; Pratiwi, Hasih
Prosiding University Research Colloquium Proceeding of The 15th University Research Colloquium 2022: Bidang MIPA dan Kesehatan
Publisher : Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

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

Abstract

Regression analysis is used to determine the relationship between the dependent and independent variables with a parameter estimator. The parameter estimator that is usually used is the Least Squares Method (LSM), this requires a classical assumption test. Some cases have normality assumptions that are unfulfilled because there are outliers so the result regression parameter estimates are not accurate so that robust regression is used in the analysis. Robust regression is a regression analysis method that can withstand outliers. The purpose of this study is the application of robust regression estimation Method of Moment (MM) with Tukey Bisquare weighting in the case of data on the number of maternal deaths in Indonesia 2020 with the number of maternal deaths as a dependent variable, and with independent variables such as the number of pregnant women who experience bleeding, the number of diabetics in pregnancy, and the number of HIV positive in pregnancy. The result showed that every one unit increase of three independent variables had a positive effect on the number of cases of maternal deaths, each of which was 2,8064; 2,5014; 1,1577.
Early Detection Of Currency Crisis In Indonesia Using A Combination Of Volatility And Markov Switching Models Based On Export Indicators Hasanah, Efi Yatun; Sugiyanto, Sugiyanto; Susanti, Yuliana
Jurnal Indonesia Sosial Teknologi Vol. 4 No. 3 (2023): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v4i3.607

Abstract

The currency crisis that occurred in Indonesia in 1997/1998 and 2008 had a significant impact on the Indonesian economy. An early detection system for crises is necessary to minimize the impact of such crises. One model that can detect currency crises is a combination of volatility and Markov switching models. There are several indicators that can be used to detect currency crises in a country, and one of them is exports. Research results indicate that the best combination of volatility and Markov switching models for the export indicator is MS-ARCH (2,2) with an assumption of two states. The crises of 1997/1998 and 2008 can be detected using the smoothed probability values with certain limits. Predictions for the period of July 2022-June 2023 based on the export indicator show no signs of a crisis in Indonesia.
COMPARISON OF CLUSTERING EARTHQUAKE PRONE AREA IN SUMATRA ISLAND USING K-MEANS AND SELF-ORGANIZING MAPS Ardiyani, Faradilla; Sulandari, Winita; Susanti, Yuliana
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0017-0030

Abstract

An earthquake is a sudden vibration on the earth's surface caused by the shifting of tectonic plates. One region in Indonesia that is particularly prone to earthquakes is Sumatra Island, due to its geographical location at the convergence of two tectonic plates, namely the Indo-Australian plate, which is actively subducting beneath the Eurasian plate. While earthquakes cannot be prevented or avoided, effective disaster mitigation strategies can help minimize the impact. The purpose of this research is to classify earthquake-prone areas on Sumatra Island based on depth and magnitude, allowing for further analysis to determine the characteristics of the clustering results. The study employs two clustering methods to analyze earthquake data from 1973 to 2024: the K-means and Self-Organizing Maps (SOM) algorithm. K-means algorithm is preferred for its simplicity and efficiency in handling large datasets, and suitability for numerical earthquake data analysis. Conversely, the SOM algorithm offers more stable clustering results and preserves the topological structure of the data, making it advantageous for exploring spatial patterns. The research findings indicate that the K-means algorithm provides better grouping, achieving a Silhouette Coefficient of 0.53, compared to 0.47 for the SOM algorithm. The K-means clustering resulted in two clusters: Cluster 1 contains 1,213 members and is characterized by shallow depths (3.9 km-41 km) and larger magnitudes (5 - 8.92 ), indicating a higher risk level. In contrast, Cluster 2 includes 412 members and represents areas with greater depths (40.8 km-70 km) and smaller magnitudes (5 - 6.85 ), corresponding to a lower risk level. This research aims to support the government in its earthquake disaster mitigation efforts, especially on Sumatra Island.
Co-Authors Alfiah, Lufita Nur Andreas Rony Wijaya Ardiyani, Faradilla Aristiarto, Rio Arnandya, Evelyn Regita Astuti, Arinda Tri Brilianto, Maximilianus Noel Callisa, Safira Edward Bahar Elwi Handajani, Sri Sulistijowati Handayani, Sri Sulistijowati Hasanah, Efi Yatun 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 Putri Ayu Pramesti, Putri Ayu 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