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All Journal BIMASTER POSITIF Jambura Journal of Mathematics JISKa (Jurnal Informatika Sunan Kalijaga) Majalah Ilmiah Matematika dan Statistika (MIMS) Idealis : Indonesia Journal Information System Komatika: Jurnal Pengabdian Kepada Masyarakat IJCOSIN : Indonesian Journal of Community Service and Innovation Prosiding Seminar Nasional Official Statistics PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Jurnal Akademik Pengabdian Masyarakat
Andreas Rony Wijaya
Program Studi Sains Data, Fakultas Informatika, Institut Teknologi Telkom Purwokerto
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Peningkatan Kapasitas Pemahaman Warga Desa Melung Terhadap Pemasaran Melalui Internet Kholidiyah Masykuroh; Ummi Athiyah; Irmayatul Hikmah; Andreas Rony Wijaya
Indonesian Journal of Community Service and Innovation (IJCOSIN) Vol 3 No 1 (2023): Januari 2023
Publisher : LPPM IT Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/ijcosin.v3i1.941

Abstract

Internet merupakan hal penting yang tidak terelakkan lagi, baik di perkotaan maupun di pedesaan. Masyarakat cenderung membutuhkan internet untuk memenuhi kebutuhan sehari-hari baik itu sebagai media komunikasi melalui jaringan komunikasi elektronik maupun sebagai media pemenuhan mata pencaharian pokok. Desa melung merupakan desa wisata dan memiliki banyak UMKM yang dikelola oleh masyarakat desa. Masyarakat Desa Melung memiliki permasalahan yaitu belum memiliki pemahaman mengenai pemanfaatan internet secara maksimal terutama dalam hal melakukan pemasaran untuk desa wisata dan pemasaran produk UMKM masyarakat. Belum maksimalnya pemahaman warga desa melung dalam memanfaatkan internet serta kurangnya pemahaman warga mengenai internet terutama dalam hal pemasaran, melatar belakangi kegiatan sosialisasi dalam pengabdian masyarakat ini. Metode yang digunakan adalah workshop, dengan materi mengenai literasi teknologi, pengenalan internet, dan internet marketing. Berdasarkan kegiatan yang telah dilakukan dan juga hasil survei, diperoleh kesimpulan bahwa pemahaman masyarakat sesudah dilaksanakan workshop menunjukan baik sekali, ditunjukan dengan nilai rata-rata pemahaman 4.3 dari skala 1-5 dan terjadi peningkatan pemahaman peserta tentang pemasaran menggunakan internet ditunjukan dengan kenaikan persentase kenaikan pemahaman masyarakat berkisar dari 10 – 24 % dan rata-rata peningkatan sebesar 17.2%, peningkatan persentase terkecil yaitu pada pernyataan ketiga sebanyak 10% tentang keaktifan masyarakat dalam menggunakan internet, sedangkan peningkatan persentase terbesar pada penyataan keempat sebanyak 24% tentang pemahaman internet dapat digunakan dalam proses pemasaran. Dengan demikian, warga masih perlu diberikan sosialiasi dan pelatihan mendalam sehingga masyarakat Desa Melung dapat menggunakan internet untuk pemasaran
Model ETAS Spatio-Temporal pada Analisis Pemetaan Intensitas Kegempaan di Wilayah Sumatera Andreas Rony Wijaya
Jambura Journal of Mathematics Vol 5, No 1: February 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3670.036 KB) | DOI: 10.34312/jjom.v5i1.17359

Abstract

High seismic activity forces Indonesia to mitigate natural disasters to minimize the impact of these disasters. One of the mitigation efforts that can be done is to know the possibility of an earthquake occurring in an area. Earthquakes, which are random phenomena, cannot be determined with certainty. Therefore, studies in seismological statistics can be used. One of the studies in seismological statistics that discusses the occurrence of earthquakes is the epidemic-type aftershock sequence (ETAS) model. This model describes the main seismic activity followed by aftershocks. The ETAS model, which only considers the parameters of time and event magnitude, was developed into a Spatio-temporal ETAS model, which also describes spatial parameters or locations. This study used the Spatio-temporal ETAS model to analyze the Sumatra region’s seismic activity from 2000-2022. Earthquake data sources are obtained from the United State Geological Survey (USGS). Based on data analysis, the Spatio-temporal ETAS model expressed in terms of the conditional intensity function shows that earthquakes that occur in the Sumatera region with a large magnitude tend to produce a large conditional intensity function as well. The spatial component of the spatio-temporal ETAS model can be explained by mapping the peak ground acceleration (PGA). The results of the PGA mapping show that seismic activity in the Sumatera region is prone to occur along the north and west coasts of the Sumatra region, with PGA values ranging from 0.2 g to 0.9 g. The larger the PGA value means, the higher the risk of an earthquake occurring in the area marked with a reddish-yellow dot or contour.
MODEL PREDIKSI DATA HARGA MINYAK MENTAH DUNIA DENGAN METODE EXPONENTIAL SMOOTHING Andreas Rony Wijaya
Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya Vol 12, No 1 (2023): Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya
Publisher : FMIPA Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/bbimst.v12i1.61977

Abstract

Harga Bahan Bakar Minyak (BBM) menjadi suatu faktor penting yang perlu dikontrol oleh pemerintah. Harga BBM ini dipengaruhi oleh beberapa faktor, salah satunya adalah harga minyak mentah dunia. Oleh karena fluktuatifnya harga minyak mentah dunia, perlu diperhatikan perkembangan harga minyak mentah di pasar global. Peramalan harga minyak mentah dunia juga perlu dilakukan untuk memperkirakan harga minyak mentah dunia di masa depan. Pada penelitian ini dilakukan peramalan harga minyak mentah dunia untuk tahun 2023. Data yang digunakan adalah data tahun 2020 sampai dengan 2022, sebanyak 700 observasi. Peramalan dilakukan dengan metode exponential smoothing dengan menggunakan nilai alpha yang paling optimal. Diperoleh nilai optimal alpha adalah sebesar 0.21. Metrik evaluasi model peramalan yang digunakan adalah MAD, RMSE, MAPE, MPE, dan MASE. Kelima metrik evaluasi model peramalan tersebut menghasilkan nilai yang kecil yang mengindikasikan bahwa model peramalan sesuai untuk meramalkan harga minyak mentah dunia. Hasil peramalan dengan metode exponential smoothing terhadap harga minyak mentah dunia menunjukkan bahwa harga minyak mentah dunia diperkirakan akan mengalami tren naik untuk 100 periode mendatang dengan nilai berkisar diantara 75 sampai dengan 90 USD / barrel. Kata Kunci : peramalan, harga minyak mentah, harga BBM, exponential smoothing
Prediction of Central Java’s Number of Exports to Four ASEAN Countries Using the Markov Chain Analysis Ria Novita Awalia Ramadhani; Andreas Rony Wijaya; ALIFIA ZAHRA WINESTI; DESTY MAYANG PRATIWI
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.371

Abstract

Central Java is one of the provinces that has many of natural resources and extraordinary industrial potential, able to offer reliable prospects to various developed countries in ASEAN, namely Singapore, Brunei Darussalam, Malaysia, and Thailand, to become the focus of exploration attention. Therefore, a prediction is made of Central Java's exports to the four ASEAN countries in 2022 and 2023 by applying the Markov chain analysis method. The prediction results obtained that the total exports to Singapore, Brunei Darussalam, Malaysia and Thailand in a row in 2022 are 0.701, 0.001, 0.239, and 0.058. While the predictions for 2023 for the four countries are 0.540, 0.001, 0.409, and 0.050 respectively. Meanwhile, the steady state of the Markov chain is 0.3595 for Singapore, 0.0013 for Brunei Darussalam, 0.6001 for Malaysia, and 0.0389 for Thailand. The results of this prediction can assist parties involved in making economic decisions related to Central Java's exports to developed countries in ASEAN. Information regarding predictions of an increase or decrease in exports from one year to the next can be used as a reference for business people, governments and related organizations to plan more appropriate and efficient economic strategies and policies.
PREDIKSI POTENSIAL GEMPA BUMI INDONESIA MENGGUNAKAN METODE RANDOM FOREST DAN FEATURE SELECTION Tantyoko, Henri; Sari, Dian Kartika; Wijaya, Andreas Rony
IDEALIS : InDonEsiA journaL Information System Vol 6 No 2 (2023): Jurnal IDEALIS Juli 2023
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v6i2.3036

Abstract

Gempa bumi adalah suatu peristiwa alamiah yang terjadi saat terjadi pelepasan energi secara tiba-tiba dalam kerak bumi, mengakibatkan getaran dan guncangan pada permukaan bumi. Gempa bumi merupakan salah satu bencana alam yang dapat menyebabkan kerusakan fisik yang besar, dampak ekonomi yang signifikan, dan hilangnya nyawa manusia. Beberapa penyebab gempa bumi antara lain aktivitas tektonik lempeng bumi, pergerakan lempeng tektonik, dan deformasi kerak bumi. Untuk mengurangi jumlah korban jiwa, perlu dilakukan prediksi kapan gempa bumi akan terjadi di suatu wilayah. Salah satu cara untuk memprediksi ialah dengan menggunakan metode Machine Learning yaitu Random Forest (RF), metode ini memanfaatkan beberapa pohon keputusan yang selanjutnya dilakukan voting untuk menentukan keputusan akhir prediksi . Model yang baik adalah model yang menghasilkan kesalahan seminimal mungkin. Oleh karena itu, penulis melakukan skema seleksi fitur untuk mengolah fitur-fitur yang memiliki korelasi yang kuat. Prediksi menggunakan RF dengan seleksi fitur menghasilkan F1 score sebesar 92.23%, yang lebih baik 5.02% dibandingkan tanpa menggunakan seleksi fitur. Metode RF + Seleksi Fitur ini juga jauh lebih baik jika dibandingkan metode machine learning tradisional lainnya seperti SVM, Naïve Bayes, dan Decision Tree.
Peramalan Harga Cabai dan Bawang di Pasar Tradisional Purwokerto dengan Model Autoregressive Integrated Moving Average (ARIMA) Ulya, Fadilla Zundina; Wijaya, Andreas Rony; Puspita, Popy Laras
Seminar Nasional Official Statistics Vol 2023 No 1 (2023): Seminar Nasional Official Statistics 2023
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2023i1.1794

Abstract

Chilies and onions are the agricultural commodities that are commonly used in daily life. However, fluctuating chili and onion prices can cause market instability and harm farmers when prices drop sharply. Therefore, it is necessary to conduct forecasting to predict the future price of both commodities. This research aims to find the best model for predicting chili and onion prices using the Autoregressive Integrated Moving Average (ARIMA) method which is determined based on minimum AIC and BIC values. The data used in this research includes the price of large red chilies, bird's eye chilies, shallots and garlic at the Purwokerto Traditional Market from January 2022 to April 2023. The selected models are ARIMA (0,1,0), ARIMA (1,1,0 ), ARIMA (0,1,0), and ARIMA (3,1,0) respectively for large red chilies, bird's eye chilies, shallots, and garlic. The forecasting model evaluation metrics used are RMSE and MAPE. The value is small enough to indicate that the model is appropriate. Based on the forecasting results, the price of chilies and onions in the next month will tend to be constant at IDR 55,000 for large red chilies, IDR 55,086 for bird's eye chilies, IDR 45,000 for shallots, and IDR 40,000. - for the garlic.
Peramalan nilai tukar rupiah terhadap dollar Amerika menggunakan model ARIMA Wijaya, Andreas Rony
Majalah Ilmiah Matematika dan Statistika Vol 23 No 2 (2023): Majalah Ilmiah Matematika dan Statistika
Publisher : Jurusan Matematika FMIPA Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/mims.v23i2.38660

Abstract

The exchange rate of the Rupiah against the currencies of other countries is one of the factors in identifying the condition of an economic condition. The value exchange rate can be strengthened or weakened at any time, according to the country's situation. Fluctuations of the Rupiah exchange rate could affect domestic inflation. Therefore, it is necessary to plan a good policy, considering the Rupiah's movement in the future. One of the currencies that affect the world's economic conditions is the United States Dollar. This study forecasts the Rupiah's exchange rate against the United States Dollar (USD). Data is obtained from Bank Indonesia, with the period of the data used from January 2018 to August 2022. One method of forecasting time series data is the Autoregressive Moving Average (ARIMA) method. The best model is selected based on the smallest AIC and BIC values. Based on the analysis of model selection, the ARIMA(1, 0, 1) model is the best model for forecasting the exchange rate of the Rupiah against the US Dollar. The results of the prediction of the Rupiah exchange rate show that the exchange rate of the Rupiah against the US Dollar has increased and decreased, which was not significant. Keywords: ARIMA, exchange rate, forecasting, time seriesMSC2020: 62M10
Pelatihan Manajemen dan Visualisasi Data Menggunakan Excel untuk Guru Matematika SMP di Kabupaten Karanganyar: Data Management and Visualization Training using Excel for Junior High School Mathematics Teacher in Karanganyar Regency Nirwana, Muhammad Bayu; Pratiwi, Hasih; Susanti, Yuliana; Respatiwulan, Respatiwulan; Handayani, Sri Sulistijowati; Wijaya, Andreas Rony; Pratama, Alfito Putra Fajar; Ferawati, Kiki
Komatika: Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 2 (2024): November 2024
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat, Institut Informatika Indonesia Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/komatika.v4i2.1023

Abstract

Literasi statistik merupakan kemampuan untuk memahami beragam informasi statistik yang dimunculkan di berbagai media. Kemampuan ini meliputi keterampilan dalam menginterpretasikan grafik dan tabel, serta mampu membaca dan memahami statistik dalam berita, media, jajak pendapat, dan lain-lain. Kabupaten Karanganyar merupakan salah satu kabupaten di Provinsi Jawa Tengah yang berbatasan dengan Kota Surakarta dan termasuk sebagai wilayah Karesidenan Surakarta. Pengetahuan mengenai literasi statistik dan implementasinya di wilayah Kabupaten Karanganyar merupakan hal yang penting untuk disampaikan kepada masyarakat, karena berkaitan langsung dengan pemahaman mengenai informasi data statistika dan bagaimana merepresentasikannya. Sebagai ilmu yang mempelajari tentang cara pengumpulan, analisis, dan pengambilan keputusan dari data, pengetahuan tentang statistika merupakan ilmu penunjang yang penting untuk dimiliki oleh masyarakat. Sebagai sasaran peningkatan literasi statistika kali ini Grup Riset Statistika dan Sains Data Bidang Lingkungan dan Kesehatan Program Studi Statistika FMIPA UNS akan melaksanakan pengabdian masyarakat dengan bentuk pelatihan untuk guru dan siswa SMP di Kabupaten Karanganyar melalui forum Musyawarah Guru Mata Pelajaran (MGMP) Matematika. Literasi statistik memerlukan pengetahuan tentang analisis dan visualisasi data yang diberikan untuk meningkatkan pemahaman terkait penerapan metode statistika dengan menggunakan Excel yang sudah banyak dikenal oleh masyarakat.
PENERAPAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK DALAM ANALISIS SENTIMEN PENGARUH BRAND IMAGE DAN LABEL HARGA: STUDI ANALISIS: PRODUK SKINCARE SKINTIFIC Lathifah, Ekarini; Wicaksono, Aditya Dwi Putro; Wijaya, Andreas Rony
POSITIF : Jurnal Sistem dan Teknologi Informasi Vol 10 No 2 (2024): Positif : Jurnal Sistem dan Teknologi Informasi
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/positif.v10i2.2259

Abstract

There are various kinds of products that are included in cosmetic products, namely personal care, make up, fragrance including perfume, hair care, and skincare. Skincare has become one of the primary needs for women in Indonesia today, because skincare can maintain healthy skin. Skincare is a beauty product that is used by users to clean dirt on the face. In deciding to choose skincare products, of course, consumers are influenced by various factors such as skincare quality, brand image, price, and others. In addition, reviews of skincare products are also important as an effort by cosmetic companies to attract consumers' buying interest. One method in deep learning to analyze is the Convolutional Neural Network (CNN). Sentiment analysis is carried out as an effort to evaluate and determine consumer satisfaction with skincare products as well as materials for service improvement. This study uses the CNN method which in this model has several stages such as data scraping, data preprocessing which consists of data cleansing & case folding, stemming, tokenizing, filtering (stopword removal), labeling process, modeling, and model evaluation. In this study, the data used is scraping data on the Tokopedia website about skincare skintific products. The data will be processed with the CNN model to obtain an accuracy value resulting from the performance of the model.
Perbandingan Random Forest dan Convolutional Neural Network dalam Memprediksi Peralihan Pelanggan Kusuma, Dewa Adji; Dewi, Atika Ratna; Wijaya, Andreas Rony
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 2 (2025): May 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.2.186-194

Abstract

The rapid growth of the telecommunications industry has increased competition among companies for customers. As a result, customers often switch services or terminate subscriptions. Retaining customers is very important as it is 10 times cheaper than acquiring new customers. This study compares Random Forest (RF) and Convolutional Neural Network (CNN) algorithms in predicting customer switching, using Correlation-based Feature Selection (CFS) and Recursive Feature Elimination (RFE) for data partitioning. Model evaluation using Confusion Matrix and Area Under Curve (AUC). The evaluation results show that the performance of CNN models with optimization parameters is superior. Using the CFS dataset, the test data evaluation results obtained an accuracy of 98%, AUC 0,96, precision 99%, recall 92%, and f1-score 96%. The best tuning result for CNN is 3 combinations of filter and kernel {[64&7], [32&3], [16&2]} and pool_size=2. The limitation of this research is how to handle the two algorithms being compared. Both use different approaches, namely Supervised Learning and Deep Learning.
Co-Authors Aditya Dwi Putro Wicaksono Afni Arifatul Hidayah Al Khansa ALIFIA ZAHRA WINESTI Arnella Hayya Amalia Ovin Atika Ratna Dewi Bernadeta Chrisma Damai Saputri DESTY MAYANG PRATIWI Dian Kartika Sari, Dian Kartika Eva Purnama Sari Gita Amalia, Gita Handayani, Sri Sulistijowati Irmayatul Hikmah Kholidiyah Masykuroh Kiki Ferawati Kusuma, Dewa Adji Lathifah, Ekarini Neti Kurnia Sari Nirwana, Muhammad Bayu Nur Setyo Jatiningtyas Pratama, Alfito Putra Fajar pratiwi, hasih Puspita, Popy Laras Respatiwulan, Respatiwulan Ria Novita Awalia Ramadhani Tantyoko, Henri Tegar Adji Syahputra Ulya, Fadilla Zundina Ummi Athiyah Widya Putri Haryati Yuliana Susanti, Yuliana