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All Journal Saintifik : Jurnal Matematika, Sains, dan Pembelajarannya Madaniya Jurnal Dedikasi Journal of Mathematics: Theory and Applications SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam Indonesian Journal of Fundamental Sciences Sipakaraya : Jurnal Pengabdian Masyarakat Jurnal Hasil-Hasil Pengabdian dan Pemberdayaan Masyarakat (JHP2M) Journal of Mathematics, Computation and Statistics (JMATHCOS)
Muhammad Abdy
Universitas Negeri Makassar
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Other

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Pelatihan Teknologi Digital Chatbot Interaktif Berbasis Poe AI dan Mathpix bagi Guru SMA di Kabupaten Majene Muhammad Abdy; Nursyam Anaguna; Musawwir Musawwir; Muh. Rafli Rasyid; Fadhil Zil Ikram
Sipakaraya : Jurnal Pengabdian Masyarakat Vol. 4 No. 2 (2026): Sipakaraya : Jurnal Pengabdian Masyarakat
Publisher : Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/sipakaraya.v4i2.6141

Abstract

Kurangnya keterampilan guru dalam menggunakan teknologi pembelajaran digital untuk menciptakan efektifitas dan inovasi dalam mengevaluasi pembelajaran matematika yang sesuai dengan perkembangan zaman merupakan masalah yang dialami beberapa guru SMA Kabupaten Majene. Untuk mengatasi masalah tersebut, dilaksanakan kegiatan pelatihan dan pendampingan bagi guru yang bertujuan untuk: 1) meningkatkan keterampilan penggunaan teknologi digital Chatbot Interaktif berbasis Poe AI dan Mathpix dalam pembelajaran, dan 2) meningkatkan efektifitas dan inovasi guru dalam membuat soal dan rubrik penilaian melalui penggunaan teknologi digital Chatbot Interaktif berbasis Poe AI dan Mathpix. Metode pelaksanaan terdiri dari 4 tahapan yaitu: perencanaan, tindakan, observasi, dan evaluasi. Kegiatan pelatihan dilaksanakan selama 1 hari dan kegiatan pendampingan dilakukan selama 2 bulan. Peserta yang terlibat sebanyak 16 guru, di mana lokasi pengabdian dilaksanakan di SMA Negeri 2 Majene. Hasil pelatihan dan pendampingan menunjukkan terdapat peningkatan keterampilan guru dalam menggunakan teknologi digital Poe AI dan Mathpix. Selain itu, ada peningkatan efektifitas dan inovasi guru dalam menyusun soal yang bervariasi dan berkualitas dalam pembelajaran matematika melalui penggunaan teknologi Chatbot interaktif berbasis Poe AI dan Mathpix.
Implementasi Praktik Kerja Lapangan Mahasiswa Matematika di Badan Pusat Statistik Kabupaten Kolaka dalam Mendukung Pembangunan Berbasis Data Muhammad Isbar Pratama; Abdurahman Hamid; Maya Sari Wahyuni; Muhammad Abdy; Irwan Irwan
Jurnal Hasil-Hasil Pengabdian dan Pemberdayaan Masyarakat Vol. 5 No. 2 (2026): Volume 05 Nomor 02 (Mei-Juli 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/0mg3v919

Abstract

Badan Pusat Statistik (BPS) merupakan Lembaga Pemerintah Non Kementerian yang memiliki tanggung jawab langsung kepada Presiden. Kegiatan Praktik Kerja Lapangan (PKL) mahasiswa Jurusan Matematika FMIPA Universitas Negeri Makassar dilaksanakan di BPS Kabupaten Kolaka selama dua bulan, mulai 09 Juni hingga 08 Agustus 2025. Kegiatan ini bertujuan memberikan pengalaman nyata kepada mahasiswa dalam menerapkan ilmu matematika dan statistik pada dunia kerja, khususnya dalam pengolahan data, administrasi, dan sistem informasi statistik. Metode pelaksanaan menggunakan pendekatan deskriptif kualitatif partisipatif, di mana mahasiswa terlibat langsung dalam kegiatan harian kantor dan mendapat bimbingan dari pegawai BPS. Hasil kegiatan menunjukkan bahwa mahasiswa memperoleh wawasan praktis mengenai proses pengumpulan dan pengolahan data statistik, keterampilan administrasi, serta kemampuan komunikasi dan kerja sama tim. Kegiatan PKL ini memperkuat kolaborasi akademik dan profesional mahasiswa sekaligus menjadi wujud kontribusi nyata “Mappuinge Tongguno Majjapi Daerah” dalam mendukung pembangunan berbasis data di Kabupaten Kolaka.
A Mathematical Model with Time Delay for Addressing Online Game Addiction Through Family Time Intervention Based on Local Wisdom in South Sulawesi Muhammad Abdy; Syafruddin Side; Yusuf Ramadana; Andi Muh. Ridho Yusuf SAP
Journal of Mathematics, Computations and Statistics Vol. 9 No. 2 (2026): Volume 09 Issue 02 (June 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/1qyqb214

Abstract

This study analyzes the dynamics of online game addiction among FMIPA UNM students using a modified time-delayed SEAFR (Susceptible–Exposed–Addicted–Family time–Recovered) mathematical model. Online game addiction is a growing concern that threatens academic performance and social well-being. The research applies a five-stage methodology: (1) literature review of SIR-based models and digital addiction studies to identify compartments and parameters; (2) data collection from 400 students through proportionate stratified random sampling; (3) model development incorporating time delay, cultural norms, and family-based interventions; (4) equilibrium and stability analysis using Jacobian matrices and Routh–Hurwitz criteria; and (5) numerical simulations with MATLAB to evaluate intervention strategies. The analysis yields a basic reproduction number, R₀ = 0.5, indicating that addictive behavior tends to decline without reinforcement of exposure. However, the endemic equilibrium remains locally asymptotically stable, suggesting addiction may persist if interventions are weak. Simulation results highlight that reducing exposure (β), enhancing recovery through awareness and cultural support (γ), and strengthening family-based activities significantly reduce addiction prevalence. These findings show the usefulness of mathematical modeling as a decision-support tool contextualized by local wisdom, offering practical insights for policymakers and educators in designing effective interventions to address student addiction.
Forecasting Acute Respiratory Infection Incidence in South Sulawesi Province Through a Hybrid ARIMA–RBFNN Model Muthia Ramadhani Rafli; Muhammad Abdy; Wahidah Sanusi
Journal of Mathematics, Computations and Statistics Vol. 9 No. 2 (2026): Volume 09 Issue 02 (June 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/ca608g80

Abstract

Abstract. Among all notifiable diseases in Indonesia, Acute Respiratory Infection (ARI) consistently registers the highest national burden of illness. Within South Sulawesi Province alone, the eight-month tally from January through August 2023 surpassed 320,942 confirmed cases, underscoring the critical need for reliable case-number projections to guide evidence-based health-service planning. The present work constructs a time series forecasting framework that integrates ARIMA (Autoregressive Integrated Moving Average) with a Radial Basis Function Neural Network (RBFNN) under the hybrid paradigm proposed by Zhang (2003). Monthly ARI incidence data spanning January 2014 to December 2024 provided 132 observations in total. Following a chronological split, the first 96 data points (January 2014–December 2021) served as the training set and the remaining 36 (January 2022–December 2024) as the hold-out evaluation set. ARIMA captured the linear dynamics of the series, whereas RBFNN was subsequently applied to the ARIMA residuals to account for any nonlinear structure that remained unexplained. Minimum-AIC model selection identified ARIMA(2,1,2) as the most suitable linear specification. For the RBFNN stage, a four-lag input vector—derived from the partial autocorrelation function—combined with four hidden units and a multiquadratic basis function delivered the best generalisation performance. Assessed against MAPE, RMSE, and R², the standalone ARIMA(2,1,2) attained 14.19%, 5038.37, and 0.6275, respectively; RBFNN alone produced 15.47%, 4714.93, and 0.5479; and the Hybrid ARIMA–RBFNN yielded 16.11%, 5014.99, and 0.6309. The superior R² of the combined model demonstrates its enhanced capacity to account for data variability. Because all three models returned MAPE values below the 20% threshold, they qualify as good predictors under the Lewis (1982) classification scheme. On this basis, the hybrid approach is put forward as the preferred tool for ARI early-warning and surveillance operations in South Sulawesi.
Co-Authors Abdurahman Hamid Andi Muh. Ridho Yusuf SAP Aswi, Aswi Dita Mulyana Ramadhani Fadhil Zil Ikram Fadhil Zil Ikram Hikma Aulia Irwan Irwan Muhammad Farhan Muhammad Isbar Pratama Muhammad Rifandi Musawwir Musawwir Muthia Ramadhani Rafli Nasrullah Nasrullah Nurkaila Kaito Nursyam Anaguna Prayetno, Budi Rahmat Syam Rasydah Nur Tuada Rasyid, Muhammad Rafli Sopiyah Sukarna Sukarna Syafruddin Side Trys Mukarram Wahidah Sanusi Wahyuni, Maya Sari Yusuf Ramadana