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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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Combined Model of Markov Switching and Asymmetry of Generalized Seasonal Autoregressive Moving Average Conditional Heteroscedasticity for Early Detection of Financial Crisis in Hong Kong Sugiyanto, Sugiyanto; Subanti, Sri; Slamet, Isnandar; Zukhronah, Etik; Susanto, Irwan; Sulandari, Winita; Aprilia, Nabila Churin
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol 10, No 2 (2024)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.v10i2.21943

Abstract

The financial crisis in Hong Kong occurred in 1997 and 2008. To prevent a crisis or reduce the impact of a crisis, action is needed through early detection of the crisis using export indicator. The combination of Markov Switching and Asymmetric Generalized Seasonal Autoregressive Moving Average Conditional Heteroscedasticity (MS-AGSARMACH) models explains the crisis well. The results show that the MSAGSARMACH(2,1,1) model can explain past and future crises well.
Forecasting of Indonesian Crude Prices using ARIMA and Hybrid TSR-ARIMA Zukhronah, Etik; Sulandari, Winita; Subanti, Sri; Slamet, Isnandar; Sugiyanto, Sugiyanto; Susanto, Irwan
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol 10, No 2 (2024)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.v10i2.21946

Abstract

Forecasting of Indonesian crude prices (ICP) is crucial for the government and policymakers. It helps them develop appropriate economic policies, budget allocations, and energy strategies. Forecasting methods that can be used are Time Series Regression (TSR) and Autoregressive Integrated Moving Average (ARIMA). This study aims to forecast ICP using ARIMA and hybrid TSR-ARIMA models. The data used in this study is the ICP per month, from January 2017 to November 2022. The data is divided into two groups, the data from January 2017 to December 2020 is used as training data, and the data from January 2021 to November 2022 is used as testing data. The MAPE values for the testing data of the TSR-ARIMA(2,1,0) and ARIMA(2,1,0) models are 8.24% and 17.37% respectively. Based on this, it can be concluded that the TSR-ARIMA(2,1,0) model is better than the ARIMA(2,1,0) model for forecasting ICP.
Peramalan Harga Minyak Mentah Indonesia dengan Model Hibrida ARIMA–FTS Cheng Kusuma, Erin Jihan Wahyu; Zukhronah, Etik; Susanti, Yuliana
Journal of Informatics Management and Information Technology Vol. 5 No. 3 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v5i3.629

Abstract

Economic growth is a key indicator of successful economic activities, with adequate crude oil availability playing a crucial role in supporting a country's economic development. This study aims to forecast Indonesian crude oil prices using an Autoregressive Integrated Moving Average (ARIMA)–Fuzzy Time Series (FTS) Cheng hybrid model. The data utilized consists of monthly Indonesian crude oil prices from January 2013 to April 2023 for training and from May 2023 to December 2024 for testing. The training data is modeled using ARIMA, and the residuals from the ARIMA model are subsequently analyzed using the FTS Cheng approach. The hybrid ARIMA-FTS Cheng forecast is generated by combining the predictions from both the ARIMA and FTS Cheng models. The results of the study show that the hybrid ARIMA–FTS Cheng model produced an MAPE of 7.46% on the training data and 4.57% on the testing data. Therefore, the ARIMA–FTS Cheng hybrid model is considered suitable for forecasting Indonesia's crude oil prices.
PENINGKATAN LITERASI STATISTIKA : MEWUJUDKAN SANTRI CERDAS SEBAGAI UPAYA OPTIMALISASI ZAKAT DAN PEMBERDAYAAN POTENSI UMMAT Slamet, Isnandar; Zukhronah, Etik; Sulandari, Winita; Subanti, Sri; Sugiyanto, Sugiyanto; Susanto, Irwan; Isnaini, Bayutama; Afanyn Khoirunissa, Husna; Adi Wicaksono, Nanda; Indra Raditya , Dionisius
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 5 No. 3: Agustus 2025
Publisher : Bajang Institute

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

Abstract

Pemberdayaan Potensi Ummat”. Tujuan utama kegiatan adalah membekali peserta dengan pengetahuan dasar statistika sebagai alat berpikir rasional dan analitis, serta memperkuat kesadaran akan kewajiban dan keutamaan (fadhilah) zakat dalam kehidupan sosial-keagamaan. Kegiatan diikuti oleh 114 peserta, terdiri dari 102 santri dan 12 ustadz. Materi yang disampaikan meliputi statistika dasar, konsep kewajiban zakat menurut syariat Islam, serta fadhilah zakat dalam rangka pemberdayaan umat. Tim pengabdian berasal dari Grup Riset Statistika dan Sains Data Bidang Industri dan Ekonomi, Universitas Sebelas Maret (UNS). Metode pelaksanaan meliputi pre-test, penyampaian materi secara interaktif, praktik pengolahan data sederhana, diskusi aplikatif, dan post-test. Hasil evaluasi menunjukkan peningkatan signifikan pada pemahaman peserta terhadap materi yang disampaikan. Kegiatan ini diharapkan menjadi langkah awal dalam membentuk generasi santri yang cerdas secara statistik, sadar zakat, dan siap berkontribusi dalam penguatan ekonomi umat berbasis pesantren.
Perbandingan Hasil Peramalan Uang M1 di Indonesia Menggunakan Metode SARIMA dan Metode SVR Zukhronah, Etik; Hidayah, Nurrul; Susanto, Irwan
TIN: Terapan Informatika Nusantara Vol 6 No 2 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i2.7638

Abstract

M1 money is the most liquid form of money supply because all its components (currency and giral) can be directly used for daily transactions and reflect the dynamics of public consumption. M1 money forecasting is necessary to anticipate its fluctuations that can affect price stability and inflation. This study aims to compare the results of M1 money forecasting with the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Support Vector Regression (SVR) methods. The M1 Money data is divided into two, 80% training data from January 2010 to February 2021 and 20% testing data from March 2021 to December 2023. SARIMA and SVR modeling were carried out separately and then the best model was selected based on the smallest Mean Absolute Percentage Error (MAPE). The results of the study found that the best SARIMA model is SARIMA (1,1,0)(1,1,0)₁₂ with a MAPE of 2,250%, while the best SVR model uses a linear kernel with optimal hyperparameters C=100; ε=0,001; and γ=0,001 resulting in a MAPE of 2,254%. Thus, the SARIMA model has a better level of accuracy in predicting M1 money in Indonesia. The application of this model in predicting is expected to help related parties in evaluating the direction of monetary policy and understanding economic conditions.
HYBRID MODEL OF SINGULAR SPECTRUM ANALYSIS WITH AUTOREGRESSIVE INTEGRATED MOVING AVERAGE AND FUZZY TIME SERIES FOR INDONESIAN CRUDE PRICE FORECASTING Zukhronah, Etik; Sulandari, Winita; Ilahi, Esa Permata Sari Putri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1519-1526

Abstract

This study discusses a hybrid model of Singular Spectrum Analysis (SSA) with Autoregressive Integrated Moving Average (ARIMA) and Fuzzy Time Series (FTS) for forecasting the Indonesian Crude Price (ICP). SSA is considered to capture the deterministic component of the data while the ARIMA and FTS are to represent the stochastics one. The data that used in this study are ICP per month from January 2017 to May 2023. The data from January 2017 to December 2022 are used as insample data, while the data from January to May 2023 are used as outsample data. The insample data is firstly modeled by SSA and the residuals are then modeled by ARIMA, referred to as the hybrid SSA-ARIMA. By the same procedure, the hybrid SSA-FTS model is also constructed to the insample data. Based on the experiment, the hybrid SSA-ARIMA produces Mean Absolute Percentage Error values 8.08% for an insample and 7.10% for an outsample data. These values are less than those obtained by hybrid SSA-FTS. Therefore, the hybrid SSA-ARIMA is recommended for forecasting the monthly ICP.
EFFICIENCY AND ACCURACY OF CONVOLUTIONAL AND FOURIER TRANSFORM LAYERS IN NEURAL NETWORKS FOR MEDICAL IMAGE CLASSIFICATION Nafi'udin, Fauzi; Pratiwi, Hasih; Zukhronah, Etik
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2387-2396

Abstract

In an era where information flow is moving at a rapid pace, image data processing is becoming increasingly important as technology advances, including in healthcare. Convolutional Neural Network (CNN) has been a common approach in image classification, but the larger the volume of data and the complexity of the task, the more expensive the computational cost of CNN. With the rapid growth in the amount of image data, efficiency in data processing is becoming increasingly important. In this study, the performance of neural network models using the convolution layer and Fourier transform layer in medical image data classification was compared. The results show that models with a Fourier transform layer tend to provide higher accuracy and better Area Under Curve (AUC) compared to models using a convolution layer. In addition, the model with the Fourier transform layer also shows faster execution time per epoch, which indicates efficiency in data processing. However, the convolution layer has an advantage in terms of model size, although it is not significantly different from the Fourier transform layer. In conclusion, the Fourier transform layer has an advantage in the classification of medical image data.
SOLUSI CERDAS PENGELOLAAN SAMPAH KEPADA IBU-IBU PKK DI RT 23, RW 06 TLOBONGAN BENTAK, SIDOHARJO, SRAGEN Sri Subanti; Isnandar Slamet; Etik Zukhronah; Sugiyanto, Sugiyanto; Irwan Susanto; Winita Sulandari
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 4 No. 1: Juni 2024
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/jabdi.v4i1.7961

Abstract

Pengelolaan sampah adalah proses yang terstruktur, komprehensif, dan berkelanjutan yang mencakup pengelolaan dan proses reduksi sampah. Tata kelola sampah harus dilakukan secara terintegrasi dari asal hingga ke akhir agar dapat menghasilkan keuntungan secara ekonomi, perlindungan kesehatan lingkungan, dan perubahan perilaku masyarakat. Berdasarkan survei lapangan tim pengabdian Hibah Grup Riset (HGR), ibu-ibu PKK yang berlokasi di Tlobongan RT 23 RW 06 Bentak, Sidoharjo, Sragen mengalami permasalahan berkaitan dengan pengelolaan sampah. Permasalahan yang ada antara lain: banyaknya produksi sampah harian, kurangnya edukasi mengenai cara mengelola sampah, dan sampah yang terkumpul tidak dipilah. Berdasarkan analisis permasalahan mitra, tim pengabdian HGR telah memberikan solusi cerdas yaitu melalui pembuatan tempat penampungan sampah/bank untuk pemilahan sampah organik dan sampah anorganik dengan tujuan meminimalisir pencemaran lingkungan dan kegiatan pendampingan penyusunan tata prosedur pemilahan sampah organik dan anorganik
PENGELOLAAN SAMPAH DI GEDUNG TPA QURROTA A'YUN DUKUH TLOBONGAN, BENTAK, SIDOHARJO, SRAGEN Isnandar Slamet; Winita Sulandari; Irwan Susanto; Etik Zukhronah; Sugiyanto, Sugiyanto; Sri Subanti; Adigama Tri Nugraha; Aji Susanto
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 4 No. 4: September 2024
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/jabdi.v4i4.8472

Abstract

Pengelolaan sampah di Indonesia menghadapi tantangan besar akibat urbanisasi dan pertumbuhan populasi yang pesat, dengan sebagian besar sampah dibuang tanpa proses pengolahan yang memadai. Penelitian ini bertujuan meningkatkan kesadaran dan perilaku positif dalam pengelolaan sampah di TPA Qurrota A'yun melalui pendidikan karakter dan solusi pintar. Metode yang digunakan mencakup survei awal, penyusunan materi, sosialisasi, penyediaan fasilitas tempat sampah, serta pelatihan dan praktek langsung. Hasil survei awal menunjukkan rendahnya pengetahuan dan kesadaran peserta tentang pengelolaan sampah. Setelah sosialisasi, terjadi peningkatan signifikan dalam pemahaman dan perilaku peserta, dengan 50% membuang sampah di tempat yang sesuai dan 87,5% mengetahui cara memilah sampah. Kesadaran lingkungan juga meningkat menjadi 96,9%. Dampak positif terlihat dari lingkungan yang lebih bersih dan peningkatan kesadaran lingkungan serta pendidikan karakter. Hasil ini menegaskan pentingnya pendidikan dan fasilitas yang memadai dalam pengelolaan sampah yang berkelanjutan.
Model Hibrida ARIMA-Neural Network untuk Peramalan Kasus Tuberkulosis Agung Setyabudi, Arriza; Etik Zukhronah; Isnandar Slamet
Journal of Informatics Management and Information Technology Vol. 5 No. 3 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v5i3.597

Abstract

Tuberculosis (TB) remains a significant public health challenge in Surakarta City, necessitating accurate forecasting methods to support effective and planned control strategies. This study aims to evaluate the performance of the Autoregressive Integrated Moving Average-Neural Network (ARIMA-NN) hybrid model in forecasting monthly TB cases in the Surakarta region. The performance of this hybrid model is further compared with the ARIMA model. The research data used consists of monthly TB case data from January 2019 to September 2024 obtained from the Surakarta City Health Department. The data is divided into two sets: training data from January 2019 to December 2023 and testing data from January 2024 to September 2024. The ARIMA(0,1,1) model was identified as the best model for capturing the linear component of the data, yielding a Mean Absolute Percentage Error (MAPE) of 14.52% on the training data and 16.55% on the testing data. The residuals from the ARIMA(0,1,1) model were then further modeled using a Neural Network with 5 hidden neuron architecture, period lookback 6, and a learning rate of 0.1, to capture the remaining non-linear patterns. The developed ARIMA(0,1,1)-NN hybrid model showed better forecasting performance, with a MAPE value of 14.34% on the training data and 14.48% on the testing data. These results indicate that the ARIMA-NN hybrid approach offers the potential for improved accuracy compared to the ARIMA model in the context of TB case forecasting in Surakarta.
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