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All Journal JURIKOM (Jurnal Riset Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Mula Agung Barata
Universitas Nahdlatul Ulama Sunan Giri, Bojonegoro
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Peramalan Penjualan Obat dengan Menggunakan Metode Single Moving Average Alvinatul Hidayah; Mula Agung Barata; Aprillia Dwi Ardianti
Journal of Information System Research (JOSH) Vol 7 No 1 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i1.8256

Abstract

Luas Nusa Pharmacy faces challenges in managing drug inventory due to unpredictable demand fluctuations, often leading to overstocking or shortages. This situation affects operational efficiency and customer satisfaction. Therefore, a forecasting method is needed to help predict stock requirements more accurately. Forecasting is the process of estimating future needs based on historical data analysis, aimed at supporting decision-making in inventory management. This study employs the Single Moving Average (SMA) method to forecast drug stock at Luas Nusa Pharmacy. Weekly data from 10 best-selling drugs, namely Sanmol Tab, Andalan Biru, Promag Tab, Pirocam, Voltadex, Wiros, Tolak Angin, Stanza, Kalmethasone, and Antangin, were used as the basis for calculations over the past year. The study tested three forecasting periods: 3, 4, and 6 weeks. The results indicate that the 4-week period provides the most accurate prediction with the lowest error values: MAD of 34.80986, MSE of 1797.98, and MAPE of 13.80044, achieving an accuracy rate of 86.20%. The predicted drug stock for the following week, based on the 4-week period, is 224 units. With its high accuracy, the 4-week SMA method is recommended as an effective approach to help Luas Nusa Pharmacy manage drug inventory more efficiently. The implementation of this method is expected to minimize the risk of overstocking or shortages, improve operational efficiency, and ensure optimal service to the community.
Perbandingan Algoritma Machine Learning untuk Klasifikasi Kopi Menggunakan Data Sensor Electronic Nose dan Tongue Dwi Issadari Hastuti; Mula Agung Barata; Ifnu Wisma Dwi Prastya
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9349

Abstract

Coffee is a leading Indonesian commodity with a diversity of aromas and flavors influenced by variety and region of origin. However, the process of identifying and classifying coffee types is still often carried out conventionally through sensory testing, which is subjective, time-consuming, and dependent on panelist expertise. This situation encourages the need for a more objective and consistent automated approach based on sensor technology and machine learning. This study aims to compare the performance of several machine learning algorithms, namely Logistic Regression, Support Vector Classifier (SVC), and Random Forest, in classifying Indonesian coffee types using multisensor Electronic Nose and Electronic Tongue data. The data used comes from gas, temperature, and pH sensors with a total of 1,503 samples representing ten coffee classes. The preprocessing stage includes data cleaning using the Interquartile Range (IQR) method to remove outliers and noise reduction using the Moving Average method. The results show that the application of data cleaning and noise reduction significantly improves the performance of all classification models. Among the algorithms tested, Random Forest showed the most stable and superior performance in classifying coffee types. These findings confirm that the combination of appropriate data preprocessing and appropriate algorithm selection plays a crucial role in improving the accuracy of machine learning-based coffee classification systems.
Analisis Perbandingan Seleksi Fitur dalam Memprediksi Kelulusan Mahasiswa dengan Menngunakan Artificial Neural Network M. Khoirul Risqi; Ifnu Wisma Dwi Prastya; Mula Agung Barata
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9420

Abstract

Student attrition presents a major challenge in higher education due to its direct impact on academic quality and institutional graduation rates. Detecting students who are likely to withdraw at an early stage is therefore essential to ensure that timely interventions can be made. This study investigates how three distinct feature selection techniques—Chi-Square, Information Gain, and ANOVA—affect the performance of Artificial Neural Networks (ANN) in classifying student outcomes. The data used in the experiment were drawn from academic and administrative records, which had been standardized through Min-Max normalization. The results demonstrate that each method contributes positively, with classification accuracies ranging from 88.71% to 91.37%. Information Gain emerged as the most effective approach, yielding the highest accuracy at 91.37% and a recall score of 97.29%, largely due to its capability to reduce entropy and isolate the most informative variables. ANOVA also performed consistently well with 90.82% accuracy, while Chi-Square was comparatively less effective, potentially due to its reliance on categorical variables that may not capture predictive nuances. These findings emphasize the strategic importance of applying robust feature selection to improve ANN-based prediction models. Ultimately, this research supports the design of data-driven systems aimed at reducing student dropout rates and strengthening academic retention strategies across higher education institutions.
Evaluasi Pengaruh RFE Terhadap Kinerja Random Forest dengan SVM pada Klasifikasi Kemiskinan Kabupaten/Kota Indonesia Shafa Kirana Aralia; Mula Agung Barata; Ita Aristia Sa'ida
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9527

Abstract

Poverty is a socio-economic issue that remains a concern in Indonesia, with differences in development characteristics between districts/cities causing wide variations in indicators that are intercorrelated. Feature redundancy and the existence of extreme values have the potential to reduce the generalization ability of classification models and reduce the interpretability of results. Therefore, an approach is needed that not only produces high accuracy but is also capable of identifying the most relevant indicators. Therefore, an approach is needed that not only produces high accuracy but is also capable of identifying the most relevant indicators. This study aims to evaluate the effect of Recursive Feature Elimination (RFE) on the performance of Support Vector Machine (SVM) and Random Forest in classifying the poverty status of districts/cities in Indonesia. The dataset used consists of 514 observations with two target classes, namely non-poor and poor. The preprocessing stage included data cleaning and outlier handling using the IQR capping method, then the data was divided into 80% training data and 20% test data. Testing was conducted on four scenarios: SVM, SVM+RFE, Random Forest, and Random Forest+RFE. Evaluation used a confusion matrix, accuracy, precision, recall, and F1-score. The results show that RFE does not change the accuracy of SVM (0.971), but improves the performance of Random Forest from 0.981 to 0.99 and improves the precision of the minority class. The Random Forest+RFE combination is the most effective and efficient configuration for regional poverty classification.
Co-Authors Alvinatul Hidayah Aprillia Dwi Ardianti Dwi Issadari Hastuti Ifnu Wisma Dwi Prastya Ita Aristia Sa'ida M. Khoirul Risqi Shafa Kirana Aralia