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All Journal Journal of Applied Science, Engineering, Technology, and Education ARRUS Journal of Mathematics and Applied Science VARIANSI: Journal of Statistics and Its Application on Teaching and Research Abdi Kimia : Jurnal Pengabdian Masyarakat
Fahmuddin S, Muhammad
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Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mathematics Physics Social Sciences Other

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PENINGKATAN KETERAMPILAN DASAR PRAKTIKUM BAGI MAHASISWA BARU PRODI KIMIA FMIPA UNM Sudding; Harfiana Abbas, Gusma; Munawwarah; Alam, Muhammad Nur; Fahmuddin S, Muhammad
ABDI KIMIA: Jurnal Pengabdian Masyarakat Vol 2 No 2 (2025): Jurnal Edisi Juni
Publisher : Jurusan Kimia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/abdi.v2i2.8342

Abstract

Kegiatan praktikum dalam pendidikan kimia memegang peranan penting dalam membentuk keterampilan dan pemahaman mahasiswa terhadap konsep-konsep dasar kimia. Mahasiswa baru sering mengalami kesulitan dalam mengoperasikan alat laboratorium, melakukan pengukuran yang tepat, serta menerapkan prosedur kerja yang aman dan benar. Tujuan utama kegiatan ini adalah untuk meningkatkan keterampilan dasar praktikum kimia bagi mahasiswa baru melalui pelatihan intensif berbasis praktik langsung di laboratorium. Metode pelaksanaan kegiatan mencakup sesi pelatihan teoritis, demonstrasi, dan praktik mandiri dengan pendampingan oleh dosen dan asisten laboratorium. Kegiatan ini diikuti oleh 40 mahasiswa baru yang dibagi ke dalam 4 kelompok, masing-masing terdiri atas 10 peserta. Setiap kelompok mengikuti pelatihan alat laboratorium, teknik dasar pengukuran, serta prosedur keselamatan kerja. Hasil kegiatan menunjukkan peningkatan signifikan dalam pemahaman konsep dasar dan keterampilan teknis, yang tercermin dari hasil praktik dan peningkatan skor post-test peserta. Mahasiswa menyatakan bahwa kegiatan ini membantu mereka lebih percaya diri dalam menghadapi praktikum yang sesungguhnya. Kegiatan ini memberikan kontribusi penting dalam membangun kesiapan awal mahasiswa baru melalui keterlibatan aktif dalam lingkungan pembelajaran laboratorium yang terstruktur
Penerapan Extreme Learning Machine (ELM) untuk Meramalkan Laju Inflasi di Indonesia Fahmuddin S, Muhammad; Annas, Suwardi; nurismi, Nur ismi
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 03 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm92

Abstract

Inflation is generally the tendency for the prices of goods and services to rise continuously. An artificial neural network (ANN) is an information processing model that closely resembles how an organism's memory system works, such as information transmission processes in the brain. Forecasting is the activity of determining future events based on past data. A time series is a set of observations that occur consecutively in the correct amount of time based on a time index. The data used in this study are Indonesian monthly inflation data. Extreme Learning Machine (ELM) is an artificial neural network approach that uses a single hidden layer feedforward neural network architecture (SLFN). The advantages of ELM over traditional learning algorithms are learning speed, improved generalization performance, and simplified implementation. An error value of RMSE of 0.1992215 was obtained based on the analysis performed using the Extreme Learning Machine (ELM) method.
Perbandingan Metode ARIMA dan Single Exponential Smoothing dalam Peramalan Nilai Ekspor Kakao Indonesia Fahmuddin S, Muhammad; Ruliana; Mustika M, Sitti Sri
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 03 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm373

Abstract

Indonesia is a country with an open economy, one of the sources of foreign exchange needed by a country with an open economy is exports. Cocoa is one of Indonesia's main export commodities that makes an important contribution to the country's economy, but the value of Indonesian cocoa exports fluctuates, that is there are inconsistent changes from time to time. The purpose of this study is to determine the results of forecasting the value of Indonesian cocoa exports, as well as to determine the best method for forecasting. This research compares the ARIMA and Single Exponential Smoothing methods to determine the best forecasting method. The best method is selected based on the smallest MAPE value. Based on the results of data analysis, the best forecasting model using the ARIMA method is the ARIMA (1, 0, 1) model, which has a MAPE value of 10.38060%. Meanwhile, the best forecasting model using the Single Exponential Smoothing method is with α = 0.16, which has a MAPE value of 10.92874%. So that the best method for forecasting the value of Indonesian cocoa exports is the ARIMA method.
Classification Of Hypertension Using Methods Support Vector Machine Genetic Algorithm (SVM-GA) Fahmuddin S, Muhammad; Rais, Zulkifli; Yuniar, Eka Citra
ARRUS Journal of Mathematics and Applied Science Vol. 5 No. 1 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience3976

Abstract

Support Vector Machine (SVM) is a machine learning method for classifying data that has been successfully used to solve problems in various fields. The risk minimization principle used can produce an SVM model with good generalization capabilities. The problem with the SVM method is the difficulty in determining the optimal SVM hyperparameters. This research uses Genetic Algorithm (GA) to optimize SVM hyperparameters. GA optimization on SVM is used to classify hypertension. From the result of classification analysis using GA, it shows good accuracy value performance, namely 100% compared to using only SVM.
Implementation of Machine Learning Algorithm with Extreme Gradient Boosting (XGBoost) Method In Hypertension Level Classification Rais, Zulkifli; Fahmuddin S, Muhammad; Saida, Saida; Triutomo, Agung
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci4191

Abstract

The increasing number of hypertension patients and the threat of serious complications make hypertension one of the leading causes of death worldwide. Early prevention is currently considered one of the best solutions. Early prevention through early detection can be achieved by utilizing machine learning technology. XGBoost is a machine learning algorithm based on gradient boosting machines. XGBoost applies regularization techniques to reduce overfitting and has faster execution speed as well as better performance. The objective of this research is to classify hypertension levels using the XGBoost method and leveraging hyperparameter tuning for optimization. In this study, the hyperparameter optimization technique used is gridsearchCV. The evaluation results of the XGBoost classification method using the best combination of parameters show good performance, where the XGBoost model achieves an accuracy of 93.3%, Precision of 97%, Recall of 92%, F1-Score of 93%, and AUC value of 0.935. This implies that the classification of hypertension levels in patients at Pelamonia Makassar Hospital can be well or accurately classified using the XGBoost method.
IMPLEMENTASI ANALISIS REGRESI LOGISTIK DENGAN METODE MACHINE LEARNING UNTUK MENGKLASIFIKASI BERITA DI INDONESIA Fahmuddin S, Muhammad; Aidid, Muhammad Kasim; Nurliah, Muhammad Jabbar Taslim
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 03 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm116

Abstract

Perkembangan internet sangat pesat, internet menjadi sumber informasi yang mudah untuk diakses seperti halnya berita. Perkembangan ini selain membawa dampak yang positif tentu juga dampak yang negatif di dalamnya. Penelitian ini bertujuan untuk mengetahui hasil evaluasi dan tingkat akurasi klasifikasi berita di Indonesia dengan menggunakan analisis regresi logistik beserta metode supervised learning. Data yang digunakan diperoleh dari data.mendeley.com diantaranya berita dengan total berita 600. Setelah dilakukan preprocessing data, diperoleh jumlah kata dalam dataset sebanyak 104.020 kata. Setelah membagi dataset menjadi data latih sebanyak 80% atau 480 data dan data uji sebanyak 20% atau 120 data, diperoleh hasil akurasi dalam mengklasifikasi berita menggunakan analisis regresi logistik dengan metode supervised learning sebesar 78,3%.
Perbandingan Metode ARIMA dan Single Exponential Smoothing dalam Peramalan Nilai Ekspor Kakao Indonesia Fahmuddin S, Muhammad; Ruliana, Ruliana; Mustika M, Sitti Sri
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 03 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm193

Abstract

Indonesia is a country with an open economy, one of the sources of foreign exchange needed by a country with an open economy is exports. Cocoa is one of Indonesia's main export commodities that makes an important contribution to the country's economy, but the value of Indonesian cocoa exports fluctuates, that is there are inconsistent changes from time to time. The purpose of this study is to determine the results of forecasting the value of Indonesian cocoa exports, as well as to determine the best method for forecasting. This research compares the ARIMA and Single Exponential Smoothing methods to determine the best forecasting method. The best method is selected based on the smallest MAPE value. Based on the results of data analysis, the best forecasting model using the ARIMA method is the ARIMA (1, 0, 1) model, which has a MAPE value of 10.38060%. Meanwhile, the best forecasting model using the Single Exponential Smoothing method is with α = 0.16, which has a MAPE value of 10.92874%. So that the best method for forecasting the value of Indonesian cocoa exports is the ARIMA method
PENERAPAN METODE HYBRID SSA-ARIMA PADA PERAMALAN INDEKS HARGA KEBUTUHAN PERTANIAN YANG DIBAYAR PETANI DI PROVINSI SULAWESI SELATAN: indonesia Fahmuddin S, Muhammad; Ruliana; Muh. Imam Shadiq
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 2 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm317

Abstract

This study aims to determine the results and accuracy of forecasting the farmer's price index (IHDP) in South Sulawesi Province using Hybrid SSA-ARIMA. Hybrid SSA-ARIMA is a combination of two good time series methods to improve forecasting accuracy, especially for IHDP data that contains trend and seasonal elements. The data used is the South Sulawesi IHDP data from January 2019 to June 2024 which is sourced from the official website of the Central Statistics Agency. The results of the IHDP forecast in South Sulawesi for the next 12 months from July 2023 to June 2024 tend to increase with the largest increase in September 2024 of 1.184 with a forecast accuracy based on the Mean Absolute Percentage Error (MAPE) of 1.59%. This shows that Hybrid SSA-ARIMA has very good forecasting capabilities
APPLICATION OF TIME SERIES REGRESSION (TSR) AND AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) IN RICE PRODUCTION FORECASTING IN INDONESIA Fahmuddin S, Muhammad; Ruliana; Fahmi, Nurul
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 03 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm412

Abstract

Rice production plays a crucial role in supporting food security in Indonesia. The annual fluctuations in rice yield necessitate accurate forecasting methods to support agricultural planning. This study aims to forecast rice production in Indonesia using two time series forecasting approaches: Time Series Regression (TSR) and Autoregressive Integrated Moving Average (ARIMA). The data used consist of monthly rice production from January 2020 to December 2024. The analysis results show that both methods are capable of modeling the data well, with high forecasting accuracy based on the Mean Absolute Percentage Error (MAPE). The TSR model yielded a MAPE of 13.838%, while the ARIMA(2,1,0)(0,1,0)12model achieved a lower MAPE of 13.1439%, indicating that the ARIMA model provides more accurate forecasting results. This study is expected to serve as a reference for policy-making and strategic planning in rice production management in the future.
Co-Authors Aidid, Muhammad Kasim Alam, Muhammad Nur Harfiana Abbas, Gusma Muh. Imam Shadiq Munawwarah Mustika M, Sitti Sri nurismi, Nur ismi Nurliah, Muhammad Jabbar Taslim Nurul Fahmi Rais, Zulkifli Ruliana Ruliana Ruliana, Ruliana Saida, Saida Sudding Suwardi Annas Triutomo, Agung Yuniar, Eka Citra