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All Journal SAINSMAT CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Varian Inferensi Jurnal Darma Agung JINAV: Journal of Information and Visualization Quantitative Economics and Management Studies Panrannuangku Jurnal Pengabdian Masyarakat Parameter: Journal of Statistics Journal of Mathematics: Theory and Applications SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam VARIANSI: Journal of Statistics and Its Application on Teaching and Research English Language, Linguistics, and Culture International Journal (ELSTIC-IJ) Ininnawa: Jurnal Pengabdian Masyarakat Prosiding Seminar Nasional Dies Natalis Universitas Negeri Makassar Jurnal Pengabdian Masyarakat Indonesia Jurnal Hasil-Hasil Pengabdian dan Pemberdayaan Masyarakat (JHP2M) SMART: Jurnal Pengabdian Kepada Masyarakat Journal of Mathematics, Computation and Statistics (JMATHCOS) JAMP
Hardianti Hafid
Universitas Negeri Makassar
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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Comparison of Farmer Exchange Rate Index Forecasting with Decomposition and Single Exponential Smoothing Method Muthahharah, Isma; Hafid, Hardianti
Jurnal Varian Vol. 8 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i3.5491

Abstract

NTP forecasting is crucial for supporting appropriate policy-making. Therefore, this study aims to address the problem of selecting the most accurate forecasting method for predicting the Farmers’ Terms of Trade Index (FTTI). Specifically, the objective is to compare the accuracy of two time series forecasting methods, namely Decomposition and Single Exponential Smoothing (SES), in forecasting the price index received by food crop farmers for the period 2020 to 2024. Both methods were evaluated using Root Mean Square Error (RMSE) as a measure of forecasting accuracy. The results show that the Decomposition method provides better forecasting accuracy, as indicated by lower RMSE values (RMSE = 1.846) than the SES method, both with α = 0.1 (RMSE = 7.37) and α = 0.6 (RMSE = 3.23). This finding suggests that the Decomposition method is better at capturing seasonal patterns and trends in the FTTI data than the SES method, which tends to produce larger errors. 
Rainfall Classification Using Output Statistics Models Based on Classification and Regression Trees with Principal Component Analysis Preprocessing Rais, Zulkifli; Hafid, Hardianti; Bunga, Yhegi Rombe
JINAV: Journal of Information and Visualization Vol. 7 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

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

Abstract

Makassar City has a varied monsoon rainfall pattern, so rainfall prediction is an important challenge in disaster mitigation and resource management. Data mining techniques such as classification with the Classification and Regression Trees (CART) algorithm can be used to classify rainfall and analyze historical data, but the risk of overfitting high-dimensional data requires dimension reduction such as Principal Component Analysis (PCA). To improve accuracy, the Output Statistics Model (MOS) approach that combines numerical data and observations is also used. The results of dimension reduction using the Principal Component Analysis (PCA) method showed that of the initial seven variables, only three main components (, , and ) were retained because they had eigenvalues greater than 1 and were able to explain the data variance significantly. The decision tree model that was formed resulted in an accuracy rate of 72.34% in training data. Where the model can classify most of the training data into the correct rainfall category. In the data testing, the model was able to achieve an accuracy level of 71.43%, which shows that the model has good generalization ability to new data and does not experience overfitting.
Sosialisasi Sekolah Siaga Bencana (SSB) Sebagai Upaya Meningkatkan Kesiapsiagaan Siswa di SMA Athira Makassar Meliyana, Sitti Masyitah; Muthahharah, Isma; Mar'ah, Zakiyah; Hafid, Hardianti; Juhari, Agusalim
Ininnawa : Jurnal Pengabdian Masyarakat Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2026): Volume 04 Nomor 01 (Mei 2026)
Publisher : Program Studi Manajemen FEB UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/1khhs667

Abstract

Indonesia is a disaster-prone country, making early preparedness efforts essential, particularly in the school environment. This community service activity aims to improve students’ knowledge and preparedness through the socialization of the Disaster Preparedness School (Sekolah Siaga Bencana/SSB) program at SMA Athira Makassar. The methods used include interactive lectures, discussions, and evacuation simulations, with evaluation conducted using pre-test and post-test. The results show an increase in the average preparedness score from 58.9 in the pre-test to 83.8 in the post-test, indicating an improvement of 24.9 points. The highest increase was found in the understanding of evacuation procedures. These findings indicate that the SSB socialization program is effective in enhancing students’ knowledge and disaster preparedness. Therefore, this activity contributes to fostering a disaster aware culture in schools and can serve as a model for implementing education-based disaster preparedness programs.
Comparison of ARIMA, Random Forest, and Hybrid ARIMA-Random Forest Models in Forecasting Indonesian Crude Oil Prices Yeni Rahkmawati; Selvi Annisa; Hardianti Hafid; Nuramaliyah Nuramaliyah; Emeylia Safitri
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.36540

Abstract

The price of Indonesian crude oil (ICP) is highly volatile due to fluctuations in global demand, energy policies, and geopolitical tensions, making accurate forecasting challenging. This study compares three forecasting models: ARIMA, Random Forest, and Hybrid ARIMA–Random Forest. The models are evaluated using Time-Series Cross-Validation (TSCV) with MAPE, sMAPE, and RMSE as performance metrics. The results indicate that the Hybrid ARIMA–Random Forest model achieves the lowest MAPE and sMAPE, while Random Forest attains the lowest RMSE, and ARIMA exhibits the highest forecast errors. Diebold–Mariano (DM) tests confirm that ARIMA’s predictive accuracy is significantly lower than both machine-learning-based models, whereas no significant difference is found between Random Forest and the hybrid model. Out-of-sample forecasts for January–June 2026 show relatively stable price movements within 59–63 USD per barrel, with short-term fluctuations reflected in wide prediction intervals. These findings suggest that Indonesian crude oil prices contain both linear and non-linear components, which are effectively captured by the hybrid approach. Overall, the Hybrid ARIMA–Random Forest model provides the most accurate forecasts in percentage-based metrics, offering a robust and reliable tool for policymakers, investors, and market participants navigating volatile oil markets.
Pemberdayaan Ekonomi Lokal Melalui Budidaya Lebah Trigona di Desa Lantawonua Bombana Isma Muthahharah; Zakiyah Mar’ah; Hardianti Hafid; Sitti Masyitah Meliyana; Farida Islamiah
SMART: Jurnal Pengabdian Kepada Masyarakat Vol 6, No 1 (2026): April
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/smart.v6i1.83144

Abstract

Rendahnya pemanfaatan potensi vegetasi lokal di Desa Lantawonua, Kabupaten Bombana, menjadi isu utama yang melatarbelakangi perlunya diversifikasi ekonomi melalui sektor kreatif. Fokus pengabdian ini adalah pemberdayaan ekonomi keluarga melalui budidaya lebah tanpa sengat (Trigona) dengan memanfaatkan lahan pekarangan dan kebun produktif. Tujuan pengabdian ini adalah untuk meningkatkan keterampilan teknis masyarakat dalam budidaya lebah serta membentuk kemandirian ekonomi melalui kelompok usaha yang terorganisir. Metode pelaksanaan menggunakan pendekatan partisipatif dengan strategi riset aksi yang meliputi sosialisasi pemetaan wilayah vegetasi berbasis visual, pendampingan pembentukan kelompok UPPKA "Measa Laro", pelatihan teknis lapangan, serta sistem monitoring dan evaluasi berkala. Hasil pengabdian menunjukkan adanya peningkatan signifikan pada kapasitas masyarakat dalam mengelola potensi alam secara lestari, dengan tingkat keberhasilan adaptasi koloni lebah mencapai 90%. Kegiatan ini menegaskan bahwa integrasi antara edukasi teknis dan penguatan kelembagaan lokal mampu menciptakan ketahanan ekonomi desa yang berbasis pada kelestarian ekosistem.
Sosialisasi Penerapan Model Game Based Learning Berbantuan Media Booklet di SD Inpres 12/79 TA Abd. Hafid; Awaluddin Muin; Rosmalah Rosmalah; Hardianti Hafid
SMART: Jurnal Pengabdian Kepada Masyarakat Vol 6, No 1 (2026): April
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/smart.v6i1.83224

Abstract

Permasalahan yang dihadapi oleh pendidik di SD Inpres 12/79 TA adalah rendahnya konsentrasi dan partisipasi aktif siswa akibat metode pembelajaran yang kurang variatif. Kegiatan pengabdian masyarakat ini bertujuan untuk mensosialisasikan penerapan model Game Based Learning (GBL) berbantuan media booklet sebagai solusi inovatif untuk meningkatkan kualitas pembelajaran. Metode pelaksanaan kegiatan dilakukan melalui tiga tahapan sistematis, meliputi perencanaan (observasi lokasi), pelaksanaan (pemaparan materi dan simulasi), serta evaluasi respon peserta. Hasil evaluasi terhadap 15 peserta menunjukkan respon yang sangat positif, skor tertinggi sebesar 73,3% pada aspek kenyamanan belajar menegaskan bahwa metode berbasis permainan sangat efektif untuk menciptakan suasana kelas yang dinamis. Penggunaan media booklet juga dinilai sangat praktis karena tidak bergantung pada perangkat teknologi yang rumit, sehingga relevan dengan kondisi sarana prasarana sekolah. Simpulan dari kegiatan ini menunjukkan bahwa sosialisasi tersebut berhasil meningkatkan motivasi dan kesiapan pedagogik guru dalam mengimplementasikan strategi pembelajaran yang interaktif dan berpusat pada siswa.
Classification of Gen-Z Fashion Trends Based on Tiktok Social Media Activities Using the K-Means Clustering Method Isma Muthahharah; Hardianti Hafid
Journal of Mathematics: Theory and Applications Vol. 8 No. 1 (2026): Volume 8 Nomor 1 Tahun 2026
Publisher : Program Studi Matematika Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/jomta.v8i1.6302

Abstract

Fashion trends continue to evolve as times, culture and technology change. With social media playing a big role in its spread, especially among Generation Z (Gen-Z). This study aims to classify Gen-Z dressing trends based on their activities on TikTok using the K-Means Clustering method. Data was collected through web scraping techniques from the TikTok platform, including variables such as the number of likes, comments, shares, saves, and fashion-related hashtags. The clustering results showed three main clusters: cluster 1 consists of posts with very high engagement and viral tendencies, dominated by scene trends supported by major influencers. Cluster 2 has medium engagement with still dominant scene trends but comes from medium-sized accounts. Meanwhile, cluster 3 consists of posts with low engagement, dominated by casual styles that don't attract much attention. Overall, the results show that the success of Gen-Z dressing trends on social media is influenced by visual factors, the role of influencers, and interactive elements in the content.
Rice Price Forecasting in South Sulawesi Using Neural Network Autoregression (NNAR) Hardianti Hafid; Isma Muthahharah
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/vpzsvb04

Abstract

Rice is a strategic food commodity in Indonesia due to its role as the main staple food and its significant contribution to inflation and economic stability. Fluctuations in rice prices directly affect purchasing power and are often used as an important indicator in assessing macroeconomic conditions. At the regional level, South Sulawesi plays a crucial role as one of the national food barns, where price dynamics may influence food availability and distribution, particularly in Eastern Indonesia. However, rice price data often exhibit non-linear patterns and sudden fluctuations, making accurate forecasting a challenging task. This study aims to evaluate the performance of the Neural Network Autoregression (NNAR) model in forecasting monthly rice prices in South Sulawesi. The study uses secondary time series data consisting of 61 observations from January 2021 to January 2026. The NNAR model is applied to capture non-linear relationships using lag-based inputs within a feed-forward neural network framework. The model performance is evaluated using Mean Absolute Percentage Error (MAPE) under several data splitting scenarios. The results show that the best model is NNAR (1,3) with a data split of 80% training and 20% testing, producing a MAPE value of 3.572%, which indicates excellent forecasting ability. The forecasting results suggest that rice prices are expected to remain relatively stable with a slight downward trend in the upcoming period. Overall, the NNAR model demonstrates strong capability in capturing the underlying patterns of rice price data and provides reliable forecasting performance. This study contributes to the development of time series forecasting methods and provides useful insights for policymakers in managing food price stability.
Co-Authors Abd. Hafid Abd. Hafid Abdul Rahman Agusalim Juhari Ahmad Yasir Aras Ahmar, Ansari Saleh Arwini Arisandi Awaluddin Muin Awaluddin Muin Ayu Pebriyanti Bunga, Yhegi Rombe Bungatang DH, Satriani Emeylia Safitri Fahmuddin S., Muhammad Fahmuddin, Muhammad Fahmuddin, Muhammad Farida Islamiah Hafid, Abd. Haryono, Andri Herdawati, Herdawati Isma Muthahharah Isma Muthahharah Mar'ah, Zakiyah Mar’ah, Zakiyah Meliyana R, S.Pd, M.Si, Sitti Masyitah Meliyana R, Sitti Masyitah Meyliana, Sitti Masyitah Moeh Zainal Khairul Muh. Fahmuddin Muhammad Fahmuddin S Muhammad Isbar Pratama Muhammad Nusrang Muhammad Nusrang Muhammad Yusuf Muhsin Efendi Muthahharah, Isma Muthahharah, Isma Mutiara Nur Agus Salim, Nur Agus Nuramaliyah Nuramaliyah Nusrang, Muhammad Nusrang, Muhammad Rahmanda, Lalu Ramzy Rahmat H. S. Rahmat Hidayat Rais, Zulkifli Reski Wahyu Yanti Rezky Amalia Hamka Rezky, Akhmad Rezky Ramadhana T Risqi, Shopia Rosmalah Rosmalah Rosmalah Rosmalah Rosmalah Rosmalah, Rosmalah Ruliana Rustam, Sitti Nail Satriani DH Selvi Annisa Selvi Annisa Siswahyudianto Sitti Masyitah Meliyana R. Sitti Nailah Rustam Sudarmin Yati Samsyuddin Yeni Rahkmawati Zakiyah Mar'ah Zakiyah Mar’ah Zakiyah Mar’ah Zulkifli Rais