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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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Pengembangan Bahan Ajar Matematika Berbasis Kontekstual (CTL) Materi Bilangan BulatKelas IV Sekolah Dasar Negeri 3 Rensing Tahun Pelajaran 2014/2015 Susanti, Yuliana
PALAPA: Jurnal Studi Keislaman dan Ilmu Pendidikan Vol 4 No 1 (2016): Mei
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LP2M) STIT Palapa Nusantara Lombok NTB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (18.214 KB)

Abstract

Penelitian pengembangan ini bertujuan untuk mengembangkan produk berupa bahan ajar berbasis kontekstual pada pelajaran Matematika kelas IV SDN No 03 Rensing Tahun Pelajaran 2014/2015. Penelitian ini merupakan penelitian dan pengembangan (Research and Development). Penelitian pengembangan ini menggunakan model pengembangan Borg and Gall yang dilakukan dengan lima tahapan, yaitu analisis kebutuhan, perencanaan, pengembangan draf produk, uji coba lapangan, revisi produk. Metode pengumpulan data yang digunakan dalam penelitian pengembangan ini adalah angket respon siswa dan tes hasil belajar siswa. Subyek uji coba dalam penelitian ini adalah semua siswa kelas IV SDN 3 Rensing yang berjumlah 22 orang. Produk yang dikembangkan berupa buku siswa dengan pendekatan kontekstual, penilaiannya dilihat dari segi materi dan tampilan. Nilai hasil validasi buku siswa dari segi materi adalah 3,37 sedangkan dari s`egi tampilan adalah 3,12 dengan katagori “valid” dan layak untuk digunakan berdasarkan revisi dari masing-masing validator. Persentase siswa yang memberikan respon positif terhadap buku siswa mencapai 54,6% dan data yang diperoleh dari tes hasil belajar siswa mencapai 77%. Koefisien determinasi (R2) = 0,69. Artinya 69 % pengaruh pengembangan bahan ajar matematika berbasis kontekstual terhadap hasil belajar siswa. Sehingga dapat disimpulkan dalam penelitian ini pengembangan bahan ajar matematika berbasis kontekstual dengan menggunakan model Borg and Gall dikatakan Valid dan Efektif terhadap pembelajaran pada materi bilangan bulat kelas IV SDN 3 Rensing Tahun Pelajaran 2014/2015.
The Factors Affecting Soybean Production in Indonesia Using Robust Regression with Least Median of Squares (LMS) Estimation Ratri, Aninda Puspa; Susanti, Yuliana; Slamet, Isnandar
Nusantara Science and Technology Proceedings Join Proceeding "Basic and Applied Science Conference (BASC) 2021 & 1st Education Research and Appli
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2021.1110

Abstract

Soybean is a product with a source of protein that improves the nutrition of Indonesians. The demand for soybeans is increasing, but the domestic production is not sufficient, so that the soybean production in Indonesia must be increased. This study aims to determine the influential factors on soybean production in Indonesia. The data of soybean production in Indonesia had outliers. Outliers cause the residual is not normally distributed so that the assumption of normality is violated. This problem was solved using robust regression. The estimation used was the Least Median of Squares because this estimation has a quite large breakdown point value. The results of the study show that the soybean production in Indonesia was influenced by the field area, the number of soybean seeds, and rainfall. The most influential factor on soybean production is the number of soybean seeds, field area, and rainfall. The attempts that must be conducted by the government to increase soybean production are by having socialization about soybean cultivation and ensuring the availability of soybean seeds in Indonesia.
Modeling of Rice Production in Indonesia Using Robust Regression with The Method of Moments (MM) Estimation Nugrahani , Isnaini Dyah; Susanti, Yuliana; Qona’ah, Niswatul
Nusantara Science and Technology Proceedings Join Proceeding "Basic and Applied Science Conference (BASC) 2021 & 1st Education Research and Appli
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2021.1111

Abstract

Indonesia is an agricultural country with the majority of the people making rice which is then processed product of rice as a staple food. However, in the last few years, rice production in Indonesia has decreased. Rice production data with influencing factors, namely, rice harvest area, land area affected by plant pests (OPT), rainfall, the population in Indonesia have outliers and have residuals that are not normally distributed so that regression analysis with the least-squares method cannot be used to estimate the amount of rice production. A robust regression model with Method of Moments (MM) estimation is used to solve outlier problems and violations of normality assumptions. This study aims to determine the robust MM estimation regression model to estimate rice production in Indonesia and determine the factors that significantly influence. The robust regression model of MM estimation on rice production in Indonesia shows that the increase in the amount of harvested area , the land area is exposed to plant pests (OPT) and the population will increase the amount of rice production, while the rainfall will reduce the amount of rice production with a high level of confidence. The variable harvested land area and the population has a significant effect on the amount of rice production. Based on the results obtained, it is hoped that there will be policies that consider factors that influence rice production to increase the amount of rice production in Indonesia.
Efektivitas Penggunaan Metode Eksperimen dalam Meningkatkan Kemampuan Mengenal Warna di Kelas B RA NW Rensing Bat Susanti, Yuliana
ALSYS Vol 2 No 3 (2022): MEI
Publisher : Lembaga Yasin AlSys

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (377.663 KB) | DOI: 10.58578/alsys.v2i3.466

Abstract

This study aims to improve the ability to recognize colors by using experimental methods. Children basically like to do new things and learn something interesting, namely by experimenting or experimenting, especially at the level of early childhood whose curiosity is very high. This research is a Classroom Action Research (CAR) using qualitative methods. The research data were obtained by using observation, interview and documentation techniques. The increase in the color recognition of students is marked by the increasing number of positive columns filled in for each indicator set. The research obtained is (1) the indicator of "Mentioning Colors" from 28 students, 19 (68%) positive columns filled in the pre-cycle, 22 (79%) columns in the first cycle and 25 (89%) columns in the second cycle, (2) The indicator "Delivering Experimental Results" from 28 students, 12 (42%) positive columns filled in the pre-cycle, 17 (61%) columns in the first cycle and 24 (85%) columns in the second cycle, (3) The indicator "groups colors" from 28 students 15 (64%) positive columns filled in the pre-cycle, 15 (71%) columns in the first cycle and 26 (93%) columns in the second cycle. Based on the results of the study, it can be concluded that the effectiveness of using the experimental method can improve the ability to recognize children's color in Class B RA NW Rensing Bat.
Penggunaan Media dalam Pembelajaran Tematik Terpadu di MI NW Repok Are Susanti, Yuliana
ANWARUL Vol 2 No 5 (2022): OKTOBER
Publisher : Lembaga Yasin AlSys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/anwarul.v2i5.801

Abstract

This study aims to provide an understanding of the importance of using media in integrated thematic learning in Class II MI NW Repok Are Academic Year 2021/2022. This type of research is descriptive qualitative research. Data collection techniques in this study used Observation, Interview, Documentation, and Triangulation / Combined techniques and this data collection technique was through: interview sheets, observation sheets, student activity sheets, observation sheets of student learning activities and teacher observation sheets. The results of the study show that the media plays an important role in the teaching and learning process, especially in integrated thematic learning. Media that is made as well as possible will make students more interested in learning. Learning media can also facilitate the delivery of material and support the continuity of thematic learning, especially for second grade students at the Madrasah Ibtidaiyah level. Data analysis in this study is data codification, data presentation, and drawing conclusions. Based on data analysis, data was obtained that through the use of media in thematic learning it was easier for teachers to carry out their roles as facilitators, models and role models, educators, and supervisors. In addition, learning outcomes, interest and student learning motivation are also increasing
Pemodelan Produksi Padi di Provinsi Jawa Timur dengan Regresi Non Parametrik B-Spline Handajani, Sri Sulistijowati; Pratiwi, Hasih; Susanti, Yuliana; Respatiwulan, Respatiwulan; Nirwana, Muhammad Bayu; Mahmudah, Arik
PYTHAGORAS Jurnal Pendidikan Matematika Vol 18, No 2: December 2023
Publisher : Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/pythagoras.v18i2.67475

Abstract

Kebutuhan pangan merupakan kebutuhan primer masyarakat yang harus terpenuhi. Makanan pokok yang banyak dikonsumsi masyarakat Indonesia salah satunya beras. Beras yang berasal dari padi selalu diusahakan memenuhi untuk kebutuhan konsumsi masyarakat terutama di sekitarnya. Jawa Timur adalah salah satu provinsi penyumbang terbesar produksi padi di Indonesia.  Oleh sebab itu perlunya melihat pengaruh faktor-faktor iklim di beberapa wilayah produksi padi terbesar di provinsi Jawa Timur yaitu kabupaten Tuban, Nganjuk dan Gresik terhadap besarnya produksi padi di wilayah tersebut. Tujuan penelitian ini adalah menganalisis faktor-faktor meliputi suhu, kelembaban, curah hujan dan luas panen padi terhadap jumlah prodiksi padi. Data diambil dari website BMKG dan BPS tahun 2020-2022 di Kabupaten Tuban, Nganjuk dan Gresik. Metode analisis yang digunakan dengan memodelkan regresi non parametrik B-spline dengan beberapa kombinasi titik knot dari beberapa variable prediktor yang menghasilkan GCV terkecil dari kemungkinan banyaknya titik knot yang dicobakan. Hasil pemodelan mendapatkan knot optimum pada variabel X1 (suhu) berorde 2 dengan tiga titik knot bernilai 23,45584; 24,32467; 26,93116. Knot optimum pada variabel X2 (kelembaban) berorde 2 dengan satu titik knot bernilai 83,3828. Knot optimum pada variabel X3 (curah hujan) berorde 2 dengan dua titik knot bernilai 5,177247 dan 15,51238. Knot optimum pada variabel X4 (luas panen padi) berorde 2 dengan satu titik knot bernilai 16939,25. Nilai GCV minimum yang diperoleh adalah 18462458. Hasil analisis menunjukkan semua variable berpengaruh signifikan walaupun untuk variable iklim terdapat beberapa segmen yang kurang signifikan, dengan nilai adjusted R-Square sebesar 0,987. The need for food is a primary requirement of society that must be fulfilled. One of the staple foods widely consumed by the Indonesian society is rice. Rice, which comes from paddy fields, is always cultivated to fufill  the consumption needs of the community, especially in the surrounding areas. East Java is one of the largest contributors to rice production in Indonesia. Therefore, it is necessary to examine the influence of climate factors in several rice-producing regions in East Java, namely Tuban, Nganjuk, and Gresik regencies, on the level of rice production in those areas. The aim of this research is to analyze factors such as rainfall, humidity, temperature, and rice cultivation area on rice production quantity.  The data was collected from BMKG (Meteorology, Climatology, and Geophysics Agency) and BPS (Central Statistics Agency) websites for the years 2020-2022 in Tuban, Nganjuk, and Gresik regencies. The analysis method used involves modeling non-parametric B-splines with various combinations of knot points from multiple predictor variables, resulting in the smallest Generalized Cross-Validation (GCV) among the possible knot points tested. The modeling results obtained the optimal knots for variable X1 (temperature) of order 2 with three knot points at values 23.45584, 24.32467, and 26.93116. The optimal knot for variable X2 (humidity) of order 2 was at one knot point with a value of 83.3828. The optimal knots for variable X3 (rainfall) of order 2 were two knot points with values of 5.177247 and 15.51238. The optimal knot for variable X4 (rice cultivation area) of order 2 was at one knot point with a value of 16,939.25. The minimum GCV value obtained was 18,462,458. The analysis results indicate that all variables have a significant influence, although for climate variables, there were some segments that were less significant, with an value adjusted R-Square of 0.987.
Analisis Sentimen terhadap Kalimat Finansial pada FiQA dan The Financial PhraseBank Brilianto, Maximilianus Noel; Susanti, Yuliana; Zukhronah, Etik
PYTHAGORAS Jurnal Pendidikan Matematika Vol 18, No 1: June 2023
Publisher : Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/pythagoras.v18i1.59760

Abstract

Analisis sentimen atau bisa disebut juga opinion mining merupakan salah satu tugas utama dari Natural Language Processing (NLP) yang merupakan studi komputasi yang mempelajari tentang pendapat seseorang terhadap suatu topik bahasan atau entitas. Analisis dilakukan dengan algoritma machine learning (pembelajaran mesin) Naïve Bayes, Decision Tree, dan K-Nearest Neighbor dengan membagi sentimen ke dalam dua kategori sentimen yaitu sentimen positif dan sentimen negatif. Data analisis diambil dari Financial Opinion Mining and Question Answering (FiQA) dan The Financial PhraseBank yang terdiri dari 4.840 kalimat yang dipilih dari berbagai berita keuangan dan dianotasi oleh 16 annotator berbeda yang berpengalaman dalam domain finansial. Penelitian ini ditujukan untuk mendapatkan hasil analisis sentimen dengan algoritma terbaik melalui perbandingan performa algoritma machine learning Naïve Bayes, Decision Tree, dan K-Nearest Neighbor terhadap kalimat finansial yang disajikan oleh FiQA dan The Financial PhraseBank. Berdasarkan analisis, didapatkan hasil performa dari masing-masing algoritma dengan nilai akurasi algoritma Naïve Bayes sebesar 78,45%; algoritma Decision Tree dengan nilai akurasi sebesar 77,72%; algoritma K-Nearest Neighbor (k=3) dengan nilai akurasi sebesar 41,25%; dan K-Nearest Neighbor (k=5) dengan nilai akurasi sebesar 37,38%. Analisis sentimen dengan algoritma Naive Bayes memiliki performa paling baik dengan nilai akurasi paling tinggi. Sentiment analysis or can also be called opinion mining is one of the main tasks of Natural Language Processing (NLP) which is a computational study that studies a person's opinion on a topic or entity. The analysis was performed with machine learning algorithms Naïve Bayes, Decision Tree, and K-Nearest Neighbor by dividing sentiment into two categories of sentiment namely positive sentiment and negative sentiment. The analysis data was taken from Financial Opinion Mining and Question Answering (FiQA) and The Financial PhraseBank which consisted of 4,840 sentences selected from various financial news and annotated by 16 different annotators experienced in the financial domain. This research is aimed at obtaining sentiment analysis results with the best algorithms through comparison of the performance of Naïve Bayes, Decision Tree, and K-Nearest Neighbor machine learning algorithms against financial sentences presented by FiQA and The Financial PhraseBank. Based on the analysis, the performance results of each algorithm were obtained with the accuracy value of the Naïve Bayes algorithm of 78,45%; Decision Tree algorithm with an accuracy value of 77,72%; K-Nearest Neighbor algorithm (k=3) with an accuracy value of 41,25%; and K-Nearest Neighbor (k=5) with an accuracy value of 37,38%. Sentiment analysis with the Naive Bayes algorithm (K=5) performs best with the highest accuracy values.
Retinopathy Classification using Convolutional Neural Network Method with Adaptive Momentum Optimization and Applied Batch Normalization Slamet, Isnandar; Susilotomoa, Dhestahendra Citra; Zukhronah, Etik; Subanti, Sri; Susanto, Irwan; Sulandari, Winita; Sugiyanto, Sugiyanto; Susanti, Yuliana
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.309

Abstract

Retinopathy is a common eye disease in Indonesia, ranking fourth after cataracts, glaucoma, and refractive errors. It can be overcome by early diagnosis with optical coherence tomography (OCT), but this imaging technique takes much time. In this research, retinal imaging was carried out using an expert system. The expert system in this study was formed using the convolutional neural network (CNN or ConvNet) method. CNN is an algorithm of deep learning that uses convolution operations to process two-dimensional data, such as images and sounds. This research consisted of 4 stages: data collection, preprocessing, model design, and model testing. A CNN model was formed with three arrangements, consisting of two convolutional layers and one pooling layer. The ReLU activation function, zero padding, and batch normalization were used in all three formats. Then, the flattening process was carried out, and the Softmax activation function was used at the end of the architecture. The model was built using eight epochs, and optimization of Adaptive Momentum resulted in a 98.96% test data accuracy value. The result was considered good so that CNN could be used as an alternative in retinopathy diagnosis. Further research is suggested to use other optimizations or other model architectures.
SIMULATION OF DISCRETE-TIME MARKOV CHAIN SUSCEPTIBLE VACCINATED INFECTED RECOVERED SUSCEPTIBLE (DTMC SVIRS) STOCHASTIC EPIDEMIC MODEL ON THE SPREAD OF TUBERCULOSIS DISEASE IN CENTRAL JAVA Arnandya, Evelyn Regita; Respatiwulan, Respatiwulan; Susanti, Yuliana
International Conference on Humanity Education and Society (ICHES) Vol. 3 No. 1 (2024): Third International Conference on Humanity Education and Society (ICHES)
Publisher : FORPIM PTKIS ZONA TAPAL KUDA

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Abstract

One of the infectious diseases that is still a public health challenge in Indonesia is tuberculosis (TB). This study is intended to model the spread of TB disease in Central Java using the Discrete-Time Markov Chain Susceptible Vaccinated Infected Recovered Susceptible (DTMC SVIRS) stochastic epidemic model. This model categorizes the population into four groups: susceptible, vaccinated, infected, and recovered. The transition probabilities between these groups are obtained based on transmission, vaccination, vaccine failure, vaccine effectiveness, recovery, and waning immunity rates. Parameter values were estimated using TB data from the Central Java Health Profile. Simulations were performed with different transmission rate treatments to analyze their effect on epidemic dynamics. The results show that the higher transmission rate, the longer it takes to reach the peak of epidemic and the more individuals are infected, which indicates a more serious epidemic. The model predicts that the epidemic will continue timelessly due to waning immunity and remaining susceptibility. The SVIRS model provides an overview of the spread of TB in Central Java.
COMPARISON OF ROBUST REGRESSION RESULTS OF SCALE (S) ESTIMATION AND METHOD OF MOMENT (MM) ESTIMATION ON THE CLOSING PRICE OF ENERGY SECTOR STOCKS IN 2022 Hilyatul Hilwy, Sarah; Susanti, Yuliana; Nirwana, Muhammad Bayu
International Conference on Humanity Education and Society (ICHES) Vol. 3 No. 1 (2024): Third International Conference on Humanity Education and Society (ICHES)
Publisher : FORPIM PTKIS ZONA TAPAL KUDA

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

Abstract

The development of the company is undoubtedly inseparable from financial factors. The company will issue shares that investors will purchase. Investors will consider the state of the company they invest in investment activities. Fundamental analysis can assess the company's condition by calculating company ratios. The existence of fundamental analysis can help investors make decisions. Capital market movements often experience fluctuations or extreme events in the stock market that cause outliers in stock price data. Outliers in the data can be overcome by using robust regression to reduce the impact of outliers on the data. This analysis uses S and MM estimations with Tukey Bisquare weights to estimate the model. Energy sector stock closing price data will be tested for classical assumptions, including normality, homoscedasticity, autocorrelation, and multicollinearity tests. If the energy sector stock closing price data does not meet normality, detect outliers and continue estimating data using S and MM estimations. The best model to estimate the data is the MM estimation with an adjusted R-Square value of 99.86%, fulfilling the parameter significance test, namely the t-test and F-test.
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