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All Journal TEKNIK INFORMATIKA Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIKO (Jurnal Informatika dan Komputer) JPM (Jurnal Pemberdayaan Masyarakat) SMARTICS Journal Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURIKOM (Jurnal Riset Komputer) Journal on Education Jurnal Telematika JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Informatika Global Jurnal Review Pendidikan dan Pengajaran (JRPP) INTI Nusa Mandiri Jurnal Sistem Informasi dan Informatika (SIMIKA) Vocatech : Vocational Education and Technology Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Darmabakti : Junal Pengabdian dan Pemberdayaan Masyarakat Jurnal Pendidikan dan Teknologi Indonesia Decode: Jurnal Pendidikan Teknologi Informasi Indonesian Community Journal Jurnal Algoritma Jurnal Bisnis Kolega Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics INOVTEK Polbeng - Seri Informatika
Mula Agung Barata
Universitas Nahdlatul Ulama Sunan Giri
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Digitalisasi Tata Kelola Desa Kedungprimpen Melalui Aplikasi Sistem Administrasi Persuratan dan Inventaris Aset Mula Agung Barata; Ridlwan Hambali; Ifnu Wisma Dwi Prastya; Shofiatuz Zulfia; Teguh Pribadi
Jurnal Pemberdayaan Masyarakat Vol 11 No 1 (2026): Mei
Publisher : Direktorat Penelitian dan Pengabdian kepada Masyarakat (DPPM)

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Abstract

Kedungprimpen Village faces various administrative challenges due to the manual management of correspondence and asset inventory, resulting in duplicated letter numbers, delayed services, and inaccurate village asset data. This community service program aims to develop and implement SI-Desaku, an integrated web- and desktop-based information system designed to support village correspondence administration and asset management in a unified manner. The implementation method includes socialization, system requirements analysis, application development, field testing, technical training, intensive mentoring, and program evaluation. The results indicate that SI-Desaku successfully eliminated letter number duplication by 100%, reduced service time from 15–30 minutes to 5–10 minutes, and provided an accurate, real-time village asset database. Furthermore, the digital literacy of village officials improved significantly, as evidenced by 90% of participants being able to operate the system independently. The implementation of SI-Desaku contributes to the realization of transparent, accountable, and sustainable village governance, while also being oriented toward improving the quality of public services.
Hyperparameter Optimization pada Algoritma Decision Tree untuk Klasifikasi Penyakit Jantungd Taufik Hidayat; Mula Agung Barata; Ita Aristia Sa’ida
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3297

Abstract

Heart disease is one of the leading causes of death globally, making the development of accurate classification models based on clinical data essential to support early risk stratification. The Decision Tree algorithm is widely applied in medical analysis due to its interpretability; however, its performance is often limited by the use of default hyperparameters. This study aims to improve the performance of the Decision Tree algorithm through the application of hyperparameter optimization using a two-stage strategy. Experiments were conducted using a Kaggle dataset consisting of 918 patients with 12 clinical attributes. The data preparation stage included encoding categorical variables and evaluation using stratified 10-fold cross-validation. The baseline Decision Tree model achieved an accuracy of 79.20%, precision of 83.16%, recall of 78.76%, and an F1-score of 80.68%. The two-stage optimization involved Random Search cross-validation to explore the parameter space, followed by refinement using Grid Search cross-validation. The optimized model showed improved performance, achieving an accuracy of 83.66%, precision of 84.17%, recall of 86.42%, and an F1-score of 85.13%. To test the statistical significance of the performance improvement, a Shapiro-Wilk normality test was conducted on the difference in F1-scores, indicating a normal distribution (p = 0.233). A paired t-test showed that the increase in F1-score was statistically significant (t(9) = 4.60, p = 0.0016) with a very large effect size (Cohen’s d = 1.45).
Optimasi Jadwal Tanam Padi di Kabupaten Tuban melalui Prediksi Curah Hujan Menggunakan Random Forest Naili Nafa Khatirokimmah; Mula Agung Barata; Sahri
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9635

Abstract

Weather uncertainty due to climate change increasingly threatens rice harvest success, especially when farmers still rely on traditional forecasts that are not always accurate. This study developed a decision support system to determine the timing of rice planting based on daily rainfall predictions using Random Forest Regression. Daily climate data from the BMKG in Tuban, East Java, for the period 2022–2025 was used as the basis for training, with the addition of time features such as month, day of the year, and season to capture seasonal patterns. In East Java, the rainy season usually lasts from October to April and the dry season from May to September, but climate change has caused shifts in the timing, duration, and intensity of rainfall, making traditional seasonal classifications less reliable for determining the optimal planting time. The model was tested on 2025 data and showed improved performance compared to the baseline model. The tuned model produced an MAE of 5.78 mm, an RMSE of 9.75 mm, and a coefficient of determination (R²) of 0.177, an improvement over the baseline, which had an MAE of 6.02 mm, an RMSE of 10.16 mm, and an R² of 0.107. Although the R² value is still relatively low, the decrease in MAE and RMSE indicates that the tuned model is more accurate in predicting daily rainfall, especially in the light to moderate range, which is most relevant for planting decisions
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.
Implementasi Peramalan Stok Parfum Pada Imshop Parfum dengan Metode Weighted Moving Average Zulfiana Nur’aini; Nirma Ceisa Santi; Nur Mahmudah; Mula Agung Barata
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 2 (2025): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i2.894

Abstract

Humans are social creatures who need to communicate with each other. In this regard, it is necessary to provide comfort in a conversation. To provide this comfort, you can use perfume. Imshop Parfum is a shop that sells various choices of perfume scents, but of the various types of perfumes sold, of course there are perfumes that are the most popular and rarely purchased, this is certainly a problem if the stock of the item runs out or provides too much stock. The purpose of this study is to predict the stock of goods at Imshop Parfum. The method used in this study is the Weighted Moving Average with periods 3 and 5. The forecast results from the perfume study in June 2025 were 215 for period 3, and 212.86 for period 5. MAPE is 1.98% for period 3, and the MAPE value is 2.74% for period 5. It can be concluded that period 3 is the best and most accurate result because it has the smallest MAPE value.
Klasifikasi Stunting Pada Balita dengan Algoritma Random forest dan Support Vector machine Buyung Panigoro; Mula Agung Barata; Nur Mahmudah
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 2 (2025): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i2.904

Abstract

Stunting is a health problem in the world, many factors cause stunting in toddlers, this study aims to compare the performance of the Random forest algorithm and Support Vector machine using a private dataset with a total of 618 toddler data in the Sumberharjo area in February, August 2023-2024. Adding a combination of smote techniques to handle unbalanced data and k-fold Cross-validation. The results showed the Random forest algorithm with a stable accuracy of 95.41% after reaching 94.35%. For the Support Vector machine algorithm, it achieved an accuracy of 81.45% after being smote to 83.06% and the recal decreased to 51.16%. Random forest is more recommended for classifying stunting in toddlers with stable results compared to Support Vector machines.
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.
Rice Quality Identification Built on Indonesian Food Standards Based on Electronic Nose using Naïve Bayes Algorithm Muhammad Jauhar Vikri; Ifnu Wisma Dwi Prastya; Ucta Pradema Sanjaya; Mula Agung Barata
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/0y0xct32

Abstract

Rice is a staple food in Indonesia, where its quality is regulated by the National Food Standards outlined in National Food Agency Regulation No. 2 of 2023 on Rice Quality and Labeling Requirements. Rice is classified into four grades: premium, medium 1, medium 2, and medium 3. The widespread practice of mislabeling lower-quality rice as a premium through repackaging highlights the critical need for quality control measures. An electronic nose (e-nose) is a reliable device for food quality control. Previous studies have demonstrated its ability to classify rice into two quality grades with 80% accuracy. This study uses exponential data transformation and the Naive Bayes algorithm to enhance the classification accuracy for four rice quality grades according to national standards. The methodology includes signal acquisition, feature extraction using statistical parameters, exponential data transformation, classification, and performance evaluation. The results show that exponential data transformation improves classification accuracy to 97%. This technology can be implemented for automated quality control in milling facilities, storage warehouses, and distribution centres, ensuring consistent rice quality while enhancing supply chain efficiency. The e-nose-based model offers a fast and reliable solution, minimising reliance on human operators.
Co-Authors Abdul Aziz Affan Agung Prabowo Afril Efan Pajri Alfianto Faidatul Aldi Yumardiansyah Alvinatul Hidayah Amalia Nur Laily Amalia, Salsabila Dani Amelia Faza Andiyani, Putri Aprillia Dwi Ardianti Buyung Panigoro Deni Reskianto Deni Denny Nurdiansyah Diah nawang wulan Dina Selvi Rahmadani Dina, Intan Rachma Distira, Riski Putra Ayu Dwi Irnawati Dwi Issadari Hastuti Dwi Syafi'i, Ahmad Dwi Tiyas Novitasari Dwi Tiyas Novitasari Edi Noersasongko Eka Wahyu Andriyani Elok Fathiyatul Laili Ervina Putri Efendi Fannisa Salsabila Pratiwi Fina Indri Silfana Guruh Putro Dirgantoro Hidayah, Alvinatul Ifnu Wisma Dwi Prastya Ilmiyah, Miftakhul Indra Dharma Wijaya Indra Dharma Wijaya, Indra Dharma Ita Aristia Sa'ida Ita Aristia Sa'ida Ita Aristia Sa'ida Ita Aristia Sa'ida Jauhar Vikri, Muhammad Lambang, Rahmat Tegar Patriot Hari Levia, Zachdyna Aurelya Lindya Rossita Handoko M. Khoirul Risqi M. Ridlwan Hambali Maulani, Vicka Rizqi Moch Arief Soeleman Moh. Miftahul Choiri Moh. Muhajir Moh. Yusuf Efendi Munir, Ach Sirojul Muzakka, Moch. Arifuddin Naili Nafa Khatirokimmah Nasirudin, M. Nirma Ceisa Santi Nisa, Siti Khoirun Novitasari, Dwi Tiyas Nur Mahmudah Nur Mahmudah Nur Mahmudah Nur Saifuddin Pelangi Eka Yuwita Pradema Sanjaya, Ucta Purwanto Purwanto Putri Amelia Reza Anggapratama Rheyna Anggri Setyani Rochmatin, Novia Nur Roihatur Rohmah Roihatur Rohmah Sahri Sahri Sahri Sahri Saputra, Agus Bima Shafa Kirana Aralia Shofiatuz Zulfia Shofiatuz Zulfia Silfana, Fina Indri Sinta Ningrum Taufik Hidayat Teguh Pribadi Ucta Pradema Sanjaya Usman Nurhasan Viki Mei Adi Saputra Vita Dwi Rahmawati Wulan, Diah Nawang Yaqin, Ahmad Ainul Zainul Abidin Zakki Alawi Zulfiana Nur’aini