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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI BAREKENG: Jurnal Ilmu Matematika dan Terapan Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Tri Dharma Mandiri: Diseminasi dan Hilirisasi Riset Kepada Masyarakat (Jurnal Pengabdian Kepada Masyarakat) Basis : Jurnal Ilmiah Matematika Jurnal Pengabdian Kepada Masyarakat Radisi
Fery Widhiatmoko
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

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Pelatihan Pembelajaran Matematika Menggunakan Perangkat Lunak Matematika bagi Guru–Guru Matematika SMA/MA di Kabupaten Pasuruan Syaiful Anam; Agus Widodo; Indah Yanti; Corina Karim; Fery Widhiatmoko; Mochamad Hakim Akbar Assidiq Maulana
COMSERVA : Jurnal Penelitian dan Pengabdian Masyarakat Vol. 2 No. 7 (2022): COMSERVA : Jurnal Penelitian dan Pengabdian Masyarakat
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/comserva.v2i7.422

Abstract

Pasuruan Regency has natural resources that have the potential to be developed, especially in the fields of agriculture, plantations and tourism. In an effort to improve the quality of human resources, improving the level of education is an important thing to do. One way to increase the number of people's participation in education is to improve the quality of learning so that people are interested in taking higher education levels. Learning media with mathematics software is expected to be able to visualize abstract mathematical objects so that it can improve students' understanding and encourage student learning motivation. GeoGebra is a mathematical software to visualize abstract mathematical objects quickly and accurately and can be used as a tool to construct mathematical concepts. One of the objectives of this activity is to improve the ability and skills of mathematics teachers in SMA/MA in Pasuruan Regency in developing mathematics learning media with GeoGebra software to visualize abstract mathematical objects (geometry objects). In addition, to improve the ability and skills of mathematics teachers in SMA/MA in Pasuruan Regency in explaining mathematical material containing geometric objects by utilizing Geogebra. The results of the training showed that the ability and skills of SMA/MA teachers in Pasuruan Regency increased significantly in the development of teaching media and in explaining geometric objects by using Geogebra.
Pelatihan Penerapan Sistem Integrasi Data Kependudukan Sederhana (SIDaKS) Di Kecamatan Kota Agung Timur Tanggamus Dani Al Mahkya; Fery Widhiatmoko; Dian Anggraini; Tirta Setiawan; Febri Dwi Irawati; Meida Cahyo Untoro
Jurnal Pengabdian kepada Masyarakat Radisi Vol 2 No 1 (2022): April
Publisher : Yayasan Kajian Riset dan Pengembangan RADISI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55266/pkmradisi.v2i1.68

Abstract

System-based data recording is indispensable in all fields, including government agencies. One of the important points in data recording is system integration. Data integration has the advantage of making the flow of organizational information better. One of the problems that arise in this Community Service activity partner is the unavailability of a population data integration system from each village/village to the sub-district. This activity aims to conduct training, mentoring and demonstration of the proposed integration system. The solution that will be offered is to conduct training and socialization related to population data management and integration. This training and socialization will use Ms. Excel and Google Sheet in the process. The method used in implementing community service activities is training and mentoring as well as demonstrations related to the Implementation of the Simple Population Data Integration System (SIDaKS) in Kota Agung Timur District, Tanggamus. The training was carried out in the East Kota Agung District hall, Tanggamus. Participants who attended the activity were representatives of each village/village in the Kota Agung Timur District. There are several steps taken to support the implementation of activities. The activity took place smoothly in accordance with the applicable health protocol. And it will be held on September 30, 2021 at the Kota Agung Timur , Tanggamus
Penerapan ARIMA dan Residual Bootstrap untuk Peramalan Mortalitas Dinamis Model PLAT pada Penduduk Laki-Laki di Indonesia Fery Widhiatmoko; Danardono Danardono; Mila Kurniawaty; Amanda Nadhifa Maydika; Thessalonika Sandra Devina Nishi; Chasib Idris
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 2 (2025): JANUARY 2025
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i2.41403

Abstract

The farmer’s terms of trade of food crops subsector (NTPP) in Nusa Tenggara Timur Province has always been below 100 in 2019-2023. Food crops are a substantial agricultural subsector in which its contribution to the PDRB is significant and concerns the food adequacy of the region. NTPP is a proxy indicator to see farmers’ welfare and its value is expected to grow periodically. Therefore, predictive modeling is required to know future NTPP values and to know the purchasing power of food crop farmers. The aim of this research is to compare the accuracy of Chen and Lee model with the high order fuzzy time series for NTPP forecasting in Nusa Tenggara Timur Province. This research uses monthly data from NTPP Nusa Tenggara Timur from January 2016 to October 2024. The research results show that additions up to the 3rd order increase forecast accuracy and the Lee model is more accurate than the Chen model seen from the smaller RMSE and MAPE values. The MAPE values ​​of the 3rd order fuzzy time series Chen and Lee model are 0.5453% and 0.5088% respectively. Based on the MAPE value, the 3rd order Lee model is the most accurate in forecasting NTPP in Nusa Tenggara Timur Province.    
Perbandingan Akurasi Metode Autoregressive Integrated Moving Average dan Geometric Brownian Motion untuk Peramalan Harga Saham Indonesia Hamdani, Aldan Maulana; Widhiatmoko, Fery; Fitri, Sa'adatul
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 1 April 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i1.30760

Abstract

Investment is an activity of managing sources of funds with the goal of increasing profits within a certain period of time. The number of investors in the capital market, especially stock investments continue to increase. Stock movements result in returns that investors can obtain. Randomly fluctuating share prices make it difficult for investors to forecast share prices. This research helps investors in forecasting stock price movements based on PT. Gudang Garam Tbk. (GGRM) for the period 2022. This research aims to determine the level accuracy of the Geometric Brownian Motion (GBM) and Autoregressive Integrated Moving Average (ARIMA) methods in forecasting stock price movements. The accuracy level of the Mean Absolute Percentage Error (MAPE) for the GBM method is 1.68% and the ARIMA method forecasting results is 3.37%. The MAPE value of both methods is less than 10\%, so it can be said that both methods are best fitting and have a high level of accuracy in forecasting stock price movements. The GBM method is better at forecasting stock prices because it is more realistic for financial asset price models because it includes volatility in the model.
DEVELOPMENT OF HEALTH INSURANCE CLAIM PREDICTION METHOD BASED ON SUPPORT VECTOR MACHINE AND BAT ALGORITHM Anam, Syaiful; Guci, Abdi Negara; Widhiatmoko, Fery; Kurniawaty, Mila; Wijaya, Komang Agus Arta
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2281-2292

Abstract

Health insurance industry is very much needed by the community in handling the financial risks in the health sector. The number of claims greatly affects the achievement of profits and the sustainability of the health insurance industry. Therefore, filing claims by insurance users from year to year is important to be predicted in insurance firm. The Machine Learning (ML) method promises to be a good solution for predicting health insurance claims compared to conventional data analytics methods. Support Vector Machine (SVM) is one of the superior ML approaches. Nonetheless, SVM performance is controlled by the suitable selection of SVM parameters. The SVM parameters is typically selected by trial and error, sometimes resulting in not optimal performance and taking a long time to complete. Swarm intelligence-based algorithms can be used to select the best parameters from SVM. This method is capable of locating the global best solution, is simple to implemented, and doesn't involve derivatives. One of the best swarm intelligence algorithms is the Bat Algorithm (BA). BA has a faster convergence rate than other algorithms, for example Particle Swarm Optimization (PSO). Based on this situation, this paper offers the new classification model for predicting health insurance claim based on SVM and BA. The metrics utilized for evaluation are accuracy, recall, precision, f1-score, and computing time. The experimental outcomes show that the proposed approach is superior to the conventional SVM and the hybrid of SVM and PSO in forecasting health insurance claims. In addition, the proposed method has a substantially shorter computing time than the hybrid of SVM and PSO. The outcomes of the experiments also indicate that the new classification model for predicting health insurance claim based on the SVM and BA can avoid over-fitting condition.
Estimasi Peluang Mortalitas Stokastik Model PLAT Pada Penduduk Laki-Laki Indonesia dengan Generalized Non-Linear Models Widhiatmoko, Fery
Basis : Jurnal Ilmiah Matematika Vol. 3 No. 1 (2024): BASIS: Jurnal Ilmiah Matematika
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/basis.v3i1.1218

Abstract

Model mortalitas stokastik berguna dalam konstruksi tabel mortalitas dinamis di mana diperlukan berbagai macam data historis untuk menentukan probabilitas kematian dengan lebih akurat dan diperlukan model yang dapat digunakan untuk memprediksi nilai kematian suatu populasi di suatu tempat di masa depan. Para peneliti telah memperkenalkan berbagai model yang dianggap cocok dan baik dalam menangkap aspek kematian, salah satunya adalah model mortalitas stokastik PLAT yang merupakan gabungan dari model CBD dengan beberapa fitur yang terdapat dalam model Lee-Carter untuk menghasilkan model mortalitas yang cocok untuk semua rentang usia dan terdapat efek cohort atau variasi dari pengaruh tahun kelahiran pada model tersebut. Model kematian stokastik yang dikembangkan selama ini merupakan bentuk umum dan klasifikasi dari Generalized Age-Period-Cohort Stochastic Mortality Model. Berdasarkan klasifikasi tersebut, maka metode estimasi model mortalitas stokastik yang selama ini menggunakan metode Iterasi Newton-Rhapson juga dapat menggunakan Generalized Linear Model atau Generalized Non-Linear Model. Pendekatan ini membuat spesifikasi model dan fitting terlihat jelas dan memperluas jangkauan model yang ada. Selanjutnya Iteratively Weighted Least Square digunakan dalam proses penentuan estimasi parameter yang terbentuk.
Peningkatan Kompetensi Guru Madrasah Aliyah Di Kota Batu dalam Pembelajaran Matematika Berbasis Digital Menggunakan Artificial Intelligence (DeepSeek-Canva) Marsudi, Marsudi; Suryanto, Agus; Darti, Isnani; Widhiatmoko, Fery; Musafir, Raqqasyi Rahmatullah
TRI DHARMA MANDIRI: Dissemination and Downstreaming of Research to the Community (Journal of Community Engagement) Vol 5 No 2 (2025)
Publisher : SMONAGENES Research Center, Univeritas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtridharma.2025.005.02.164

Abstract

Kemajuan teknologi informasi membuka peluang besar bagi pengembangan pembelajaran matematika berbasis digital di Madrasah Aliyah (MA) Kota Batu, meskipun masih terdapat kesenjangan keterampilan teknologi di kalangan guru dan resistensi terhadap perubahan metode. Untuk menjawab tantangan tersebut, Tim Pengabdian Kepada Masyarakat (PKM) 2025 Departemen Matematika Universitas Brawijaya, yang terdiri atas lima dosen dan lima mahasiswa, menyelenggarakan pelatihan peningkatan kompetensi guru dalam pemanfaatan Artificial Intelligence (AI) DeepSeek dan aplikasi Canva sebagai media pembelajaran. Kegiatan berlangsung pada Jumat, 1 Agustus 2025 di MAN Batu dengan peserta 18 guru matematika MA dan MTs. Metode pelaksanaan meliputi koordinasi, penyebaran kuesioner, penyampaian materi, praktik pembuatan bahan ajar digital, diskusi interaktif, pendampingan kelompok, serta evaluasi melalui pre-test dan post-test. Hasil pelatihan menunjukkan peningkatan pengetahuan dan keterampilan guru, yaitu sebesar 8,66% pada penggunaan DeepSeek dan 14,7% pada Canva. Analisis statistik dengan paired t-test menegaskan bahwa peningkatan tersebut signifikan pada kedua materi, sehingga pelatihan terbukti efektif. Selain itu, suasana pelatihan yang aktif dan partisipatif mendorong motivasi guru untuk mengintegrasikan teknologi ke dalam proses pembelajaran. Monitoring dan evaluasi mengindikasikan bahwa program ini tidak hanya memperluas literasi digital, tetapi juga memperkuat kemampuan guru dalam merancang pembelajaran matematika yang inovatif, interaktif, dan berbasis teknologi digital. Disimpulkan bahwa pelatihan ini efektif meningkatkan kompetensi guru, meskipun diperlukan program lanjutan dengan durasi lebih panjang dan pendampingan berkelanjutan agar manfaatnya lebih optimal serta dapat diimplementasikan secara konsisten dalam praktik pembelajaran. KATA KUNCI: Artificial Intelligence; DeepSeek-Canva; kompetensi guru; pembelajaran matematika digital; pengabdian kepada masyarakat
Co-Authors Agus Suryanto Agus Widodo Amanda Nadhifa Maydika Anam, Syaiful Chasib Idris Corina Karim, Ari Andari Danardono Danardono Dani Al Mahkya Dian Anggraini Fitri, Sa'adatul Guci, Abdi Negara Hamdani, Aldan Maulana Indah Yanti Irawati, Febri Dwi Isnani Darti Marsudi Marsudi Mila Kurniawaty Mila Kurniawaty Mochamad Hakim Akbar Assidiq Maulana Musafir, Raqqasyi Rahmatullah Syaiful Anam Thessalonika Sandra Devina Nishi Tirta Setiawan Untoro, Meida Cahyo Wijaya, Komang Agus Arta