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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Jurnal Ilmu Dasar Kontinu: Jurnal Penelitian Didaktik Matematika Jurnal Teknologi Informasi dan Ilmu Komputer Indonesian Journal of Applied Statistics Journal of Mathematics and Mathematics Education (JMME) BAREKENG: Jurnal Ilmu Matematika dan Terapan International Journal of Computing Science and Applied Mathematics Business Innovation and Entrepreneurship Journal TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Applied Data Sciences Jurnal Pengabdian kepada Masyarakat Journal of Informatics Management and Information Technology Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Prosiding Seminar Nasional Official Statistics Prosiding Simposium Nasional Multidisiplin (SinaMu)
Zukhronah, Etik
Universitas Sebelas Maret
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Analisis Sentimen Review Tempat Wisata Pada Data Online Travel Agency Di Yogyakarta Menggunakan Model Neural Network IndoBERTweet Fine Tuning Muhammad Zidni Subarkah; Martina Hilda; Etik Zukhronah
Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (513.868 KB) | DOI: 10.34123/semnasoffstat.v2022i1.1246

Abstract

Tourism is a leading sector in Indonesia, one of which is D.I. Yogyakarta. In 2017, the total number of tourists visiting DIY was 5,229,298. The high intensity of tourist visits is directly proportional to data on people's preferences for tourist attractions. From this problem, authors are interested in conducting a sentiment analysis of tourist attractions with the IndoBERTweet Fine Tuning Neural Network model using Online Travel Agency (OTA) data. This analysis is intended so that the government and local tourism managers can easily take a decision or policy in increasing the comfort of tourist attractions. Based on this analysis, five tourist attractions with the highest number of visitor reviews were obtained, namely, Malioboro Street, Tamansari Water Palace, Yogyakarta Palace, Yogyakarta Smart Park, and Yogyakarta Monument. Sentiment analysis of this classification produces an accuracy value of 92.84%, with weighted average recall of 93%, precision of 92%, and F1-Score of 93%.
PENINGKATAN KEMAMPUAN GURU PEMBINA KOMPETISI SAINS NASIONAL MATA PELAJARAN MATEMATIKA SMP DI KABUPATEN KARANGANYAR Etik Zukhronah; Winita Sulandari; Sugiyanto Sugiyanto; Isnandar Slamet; Sri Subanti; Irwan Susanto
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 2 No. 8: Januari 2023
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/jabdi.v2i8.4526

Abstract

Dalam upaya meningkatkan kemampuan guru pembina kompetisi sains nasional, Dinas Pendidikan dan Kebuadayaan Kabupaten Karanganyar bekerjasama dengan tim pengabdi dari Program Studi Statistika FMIPA Universitas Sebelas Maret mengadakan bimbingan teknis materi KSN mata pelajaran Matematika pada guru-guru Pembina KSN mata pelajaran Matematika SMP di Kabupaten Karanganyar. Kegiatan bimbingan teknis yang dilaksanakan selama dua hari telah mampu meningkatkan kemampuan peserta hingga 33 %. Untuk hasil yang lebih optimal, kegiatan serupa perlu dilakukan secara rutin dan terstruktur.
PENINGKATAN JIWA WIRAUSAHA SANTRI MELALUI PELATIHAN PEMANFAATAN SAMPAH PLASTIK MENJADI PRODUK BERNILAI JUAL Etik Zukhronah; Winita Sulandari; Isnandar Slamet; Sri Subanti; Sugiyanto Sugiyanto; Irwan Susanto
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 2 No. 9: February 2023
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/jabdi.v2i9.4777

Abstract

Lack of public understanding about the proper handling of plastic waste can damage the environment. Based on the results of a survey conducted on students at the Darul Muttaqin Islamic Boarding School, Sragen, it can be seen that the waste management in the boarding school has not been carried out properly. In general, waste is directly disposed of in a landfill, without prior sorting between organic and inorganic waste. In this case, the residents of the cottage have not tried to process waste, especially plastic waste into useful products. For this reason, the service team for the Statistics Study Program FMIPA UNS held a socialization and training on the use of plastic waste into ornamental flower products. The purpose of this activity is to equip students with skills, as well as to foster an entrepreneurial spirit by marketing products from plastic waste to the general public. In the end, the success of product marketing will provide its own advantages as an alternative source of income for the students. In the future, the activities carried out consistently and sustainably will not only provide good benefits for the students but also the preservation of the surrounding environment.
ANALISIS SENTIMEN KINERJA PEMERINTAHAN MENGGUNAKAN ALGORITMA NBC, KNN, DAN SVM Hizkia Yotant Pradana; Isnandar Slamet; Etik Zukhronah
Prosiding Simposium Nasional Multidisiplin (SinaMu) Vol 4 (2022): Simposium Nasional Multidisiplin (SinaMu)
Publisher : Universitas Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/sinamu.v4i1.7869

Abstract

Pemerintahan Republik Indonesia saat ini tidak lepas dari opini publik. Beberapa lembaga survei nasional melalukan survei terhadap masyarakat Indonesia dan mendapati bahwa ada pro-kontra terhadap kinerja pemerintahan saat ini. Pro-kontra tersebut dituangkan melalui media sosial, salah satunya melalui Twitter. Tujuan penelitian ini adalah untuk mengklasifikasi sentimen masyarakat Indonesia mengenai kinerja pemerintahan saat ini menggunakan data Twitter. Data berjumlah 5.874 tweet yang diambil pada 13 Februari 2022 - 14 Maret 2022. Opini masyarakat diberikan label sentimen positif dan negatif dengan metode analisis VADER kemudian dianalisis menggunakan algoritma NBC, KNN, dan SVM. Hasil analisis menunjukkan bahwa SVM dengan kernel linier merupakan algoritma terbaik untuk penelitian ini dengan nilai akurasi sebesar 85,47%, nilai presisi sebesar 89,34%, nilai recall sebesar 90,34%, dan nilai F1-score sebesar 89,83%.Kata Kunci: Analisis Sentimen, Twitter, NBC, KNN, SVMThe current government of the Republic of Indonesia was inseparable from public opinion. Several national survey institutions conducted surveys of Indonesian society and found that there were pros and cons to the current government's performance. The pros and cons were outlined through social media, one of which was via Twitter. The purpose of this research was to classify the sentiments of the Indonesian people regarding the current government's performance using Twitter data. The data totaled 5,874 tweets taken on February 13, 2022 - March 14, 2022. Public opinion was labeled positive and negative sentiment using the VADER analysis method and then analyzed using the NBC, KNN, and SVM algorithms. The results of the analysis showed that SVM with linear kernel was the best algorithm for this study with an accuracy value of 85.47%, a precision value of 89.34%, a recall value of 90.34%, and an F1-score value of 89.83%. Keywords: Sentiment Analysis, Twitter, NBC, KNN, SVM
Analisis Sentimen terhadap Kalimat Finansial pada FiQA dan The Financial PhraseBank Brilianto, Maximilianus Noel; Susanti, Yuliana; Zukhronah, Etik
PYTHAGORAS Jurnal Matematika dan 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) Naí¯ve 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 Naí¯ve 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 Naí¯ve 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 Naí¯ve 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 Naí¯ve 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 Naí¯ve 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.
Implementation of Scale-Invariant Feature Transform Convolutional Neural Network for Detecting Distracted Driver Fhadilla, Nahdatul; Sulandari, Winita; Susanto, Irwan; Slamet, Isnandar; Sugiyanto, Sugiyanto; Subanti, Sri; Zukhronah, Etik; Pardede, Hilman Ferdinandus; Kadar, Jimmy Abdel
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.v5i3.222

Abstract

A distraction while driving a vehicle may result in fatal consequences, namely accidents that may leave road users seriously injured or even dead. In order to mitigate this risk, it is imperative to establish a distracted driver detection system that is both precise and real-time. This research focuses on the application of artificial intelligence, with a particular emphasis on deep learning, which is achieved through the utilization of the Convolutional Neural Network (CNN) model. In order to enhance the detection of inattentive drivers and produce a more precise model, a scaleinvariant feature transform (SIFT)-CNN combination is proposed. The activities of the driver while operating a vehicle are categorized into ten categories in this study. One of these categories is considered a normal condition, while the remaining nine are classified as inattentive behaviors. This study implemented Adam optimization with 64 batches, a learning rate of 0.001, and epochs of 20, 25, 50, and 100. The proposed CNNSIFT model is capable of achieving superior performance in comparison to the solitary CNN model, as evidenced by the experimental results. The CNN-SIFT model has achieved 99% accuracy and a 0.05 loss when the hyperparameter configuration is optimized for 50 epochs. The analysis indicates that the accuracy of the features obtained from CNN-SIFT can be improved by approximately 1% compared with CNN to classify the type of driver distraction behavior. The model's reliability was further enhanced by its evaluation on test data, which resulted in high accuracy, precision, recall, and F1-score values. The model's ability to accurately identify driver behavior with a high degree of reliability is demonstrated by these results, which are a positive contribution to the improvement of road safety.
Application of Generalized Space Time Autoregressive Model on Farmer Exchange Rate Data in Three Provinces of The Sumatera Island Aryani, Fadhilatul Nida; Handajani, Sri Sulistijowati; Zukhronah, Etik
Jurnal ILMU DASAR Vol 21 No 2 (2020)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/jid.v21i2.17226

Abstract

The agricultural sector has a big role in the development of the Gross Regional Domestic Product (GDP). Therefore the agricultural sector is very important. Besides the agricultural sector, the farmer's welfare also needs to be considered because the agricultural sector will be good if the welfare of farmers is good also. In measuring the level of farmers' welfare, the method used is the farmer's exchange rate. The farmer's exchange rate has a location relationship and a previous time relationship. The Generalized Space-Time Autoregressive (GSTAR) model is a good method of forecasting data that contains time series and location relationships by assuming that the data has heterogeneous characteristics. The purpose of this study is to model the farmer exchange rate data with GSTAR using normalization of cross-correlations weighting and inverse distance in three provinces namely West Sumatra, Bengkulu and Jambi Provinces. Based on data analysis, the best GSTAR model obtained by using the best weighting with the model is GSTAR (11) − I(1) using normalization of cross-correlations because the assumption of normal white noise and multivariate are fulfilled with an RMSE value of 1.097775. The best GSTAR model explains that the exchange rate of West Sumatra farmers is only the previous time, Bengkulu farmers' exchange rate is the previous time and is the exchange rates of farmers of West Sumatra and Jambi, whereas for the exchange rate of farmers of Jambi is the exchange rates of farmers of Bengkulu and West Sumatra and influenced by previous times.Keywords: GSTAR, RMSE, farmers exchange rate, normalization of cross-correlations, inverse distance.
Implementasi High Order Intuitionistic Fuzzy Time Series Pada Peramalan Indeks Harga Saham Gabungan Nugraha, Titis Jati; Sulandari, Winita; Slamet, Isnandar; Subanti, Sri; Zukhronah, Etik; Sugianto, Sugianto; Susanto, Irwan
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 2: April 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241127363

Abstract

Indeks Harga Saham Gabungan (IHSG) adalah indeks yang mengukur kinerja harga semua saham yang terdaftar di Bursa Efek Indonesia (BEI) Peramalan IHSG menjadi referensi bagi investor untuk memperoleh keuntungan di pasar modal. Penelitian ini membahas penerapan metode High Order Intuitionistic Fuzzy Time Series (HOIFTS) dalam peramalan IHSG di BEI. Metode HOIFTS melibatkan tiga indikator, yaitu derajat keanggotaan, derajat non- keanggotaan, dan fungsi skor (indeks intutionistic) sehingga model yang dihasilkan mampu menangani ketidakpastian dalam data. Tahapan penting dalam pemodelan HOIFTS adalah pada fuzzifikasi intuitionistic, penentuan relasi logika fuzzy intutionistic, dan proses defuzifikasi order tinggi intuitionistic. Penelitian ini menetapkan metode Chen, baik order satu maupun order tinggi sebagai metode pembanding untuk melihat seberapa jauh keberhasilan metode HOIFTS dalam meramalkan data bulanan IHSG. Hasil perbandingan nilai RMSE (root mean square error) dan MAPE (mean absolute percentage error) yang dihasilkan oleh ketiga model menunjukkan bahwa metode HOIFTS memiliki nilai kesalahan yang paling kecil. Dengan demikian, metode HOIFTS lebih direkomendasikan dalam peramalan IHSG dibandingkan dua metode lain yang dibahas dalam penelitian ini. 
Peramalan Banyak Penumpang dari Pelabuhan Belawan Menggunakan Model Arimax dengan Efek Variasi Kalender Effendi, Arfiyansyah Rizki; Zukhronah, Etik; Sugiyanto, S
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2019: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (497.238 KB)

Abstract

Peramalan merupakan salah satu upaya untuk memperkirakan apa yang akan terjadi pada masa mendatang berdasarkan data masa lalu. Banyak penumpang di Pelabuhan Belawan merupakan data runtun waktu. Pola data banyak penumpang di Pelabuhan Belawan sangat dipengaruhi oleh hari libur, sehingga cenderung mempunyai pola musiman. Mayoritas penduduk Indonesia beragama Islam yang mempunyai Hari Raya Idul Fitri. Penentuan Hari Raya Idul Fitri tidak mengikuti kalender masehi, tetapi berdasarkan kalender Hijriah. Perbedaan penggunaan kalender masehi dan hijriah pada suatu data runtun waktu menyebabkan adanya variasi kalender. Data yang mengandung variasi kalender dapat dianalisis menggunakan model ARIMAX. Penelitian ini bertujuan untuk memperoleh model ARIMAX terbaik dan meramalkan banyak penumpang dari Pelabuhan Belawan beberapa periode ke depan. Hasil penelitian menunjukkan bahwa model ARIMAX (0,0,1) Vt,Vt+1,Ss,t; s=1,2,...,12 merupakan model terbaik. Berdasarkan model tersebut dapat diramalkan dari bulan September 2018 sampai Agustus 2019 secara berturut turut adalah 5357, 2448, 1849, 5634, 12894, 4410, 4195, 3724, 4339, 10448, 13499, 4084 penumpang.
Co-Authors Adi Wicaksono, Nanda Adigama Tri Nugraha Agung Setyabudi, Arriza Aji Susanto Aprilia, Nabila Churin Aryani, Fadhilatul Nida Atika Amalia Brilianto, Maximilianus Noel Dewi Retnosari Dina Rohmah Dyah Auliya Agustina Effendi, Arfiyansyah Rizki Fhadilla, Nahdatul Fransisca Trisnani Ardikha Putri Handajani, Sri Sulistijowati Hidayah, Nurrul Hizkia Yotant Pradana Husna Afanyn Khoirunissa Ilahi, Esa Permata Sari Putri Indra Raditya , Dionisius Irwan Susanto Irwan Susanto Isnaini, Bayutama Isnandar Slamet Isnandar Slamet Isnandar Slamet Isnandar Slamet Isnandar Slamet, Isnandar Kadar, Jimmy Abdel Kusuma, Erin Jihan Wahyu Martina Hilda Muhammad Zidni Subarkah Nafi'udin, Fauzi Nugraha, Titis Jati Pardede, Hilman Ferdinandus Pratiwi, Hasih pratiwi, hasih S Sugiyanto Solikhah Novita Intan Sri Subanti Sugianto Sugianto Sugiyanto - Sugiyanto Sugiyanto Sugiyanto Sugiyanto Sugiyanto Sugiyanto Sugiyanto, Sugiyanto Sulandari, Winita Supriyadi Wibowo Susilotomoa, Dhestahendra Citra Tito Tatag Prakoso Winita Sulandari Winita Sulandari Winita Sulandari Yuliana Susanti Yuliana Susanti, Yuliana Yulvia Fitri Rahmawati