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All Journal TEKNIK INFORMATIKA Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIKO (Jurnal Informatika dan Komputer) SMARTICS Journal Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING 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) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Darmabakti : Junal Pengabdian dan Pemberdayaan Masyarakat Jurnal Pendidikan dan Teknologi Indonesia Indonesian Community Journal Jurnal Bisnis Kolega Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics
Mula Agung Barata
Universitas Nahdlatul Ulama Sunan Giri
Agriculture, Biological Sciences & Forestry Humanities 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 Public Health Social Sciences Other

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ANALISIS SENTIMEN PENGGUNA TWITTER TERHADAP SKINCARE DENGAN METODE SUPPORT VECTOR MACHINE (SVM) Dwi Tiyas Novitasari; Barata, Mula Agung; Yuwita, Pelangi Eka
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6297

Abstract

The Originote Hyalucera Moisturizer skincare product has attracted public attention because it offers superior quality at an affordable price. Social media, especially Twitter, is used by consumers to express opinions regarding this product, whether positive, negative, or neutral. However, the large number of reviews with various sentiments can confuse potential consumers in assessing product quality. Therefore, this study aims to understand user perception through sentiment analysis and evaluate the effectiveness of the Support Vector Machine (SVM) algorithm in sentiment classification. A total of 1,820 tweets were collected using the crawling technique with Python. The data undergoes preprocessing, including text cleaning, tokenization, stopword removal, and stemming, reducing it to 902 tweets. Key text features are extracted using Term Frequency-Inverse Document Frequency (TF-IDF). For sentiment classification, this study used the SVM algorithm, which is known as an effective method in text processing. Model evaluation showed good results with an accuracy of 87%, precision of 89%, and recall of 87%. This study provides insight into public perception of The Originote Hyalucera Moisturizer and measures the effectiveness of SVM in social media-based sentiment analysis. The results of the study can be utilized by manufacturers for more targeted marketing strategies, product quality improvement, and more effective communication in responding to opinions on social media. In addition, this study contributes to the development of machine learning-based sentiment analysis methods in the context of skincare products.
Implementation of the Random Forest Algorithm with Optuna Optimization in Lung Cancer Classification Yaqin, Ahmad Ainul; Barata, Mula Agung; Mahmudah, Nur
Sistemasi: Jurnal Sistem Informasi Vol 14, No 2 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i2.4877

Abstract

Lung cancer remains one of the leading causes of death worldwide, with many sufferers unaware of their condition until it is too late for treatment. Therefore, high-accuracy prediction methods are urgently needed for early detection of lung cancer. This research uses the Random Forest algorithm, known for its excellent performance in medical data classification. In this study, modeling was optimized by implementing hyperparameter optimization using Optuna. The results of the generated model show an accuracy rate of 98.6%, which is highly significant in the context of early lung cancer detection. Additionally, this algorithm demonstrated 100% recall for the positive class and 97% for the negative class, indicating that the model is highly effective in identifying patients who truly have lung cancer. Another advantage of this model is seen in the AUC (Area Under the Curve) value reaching 1, indicating 100% accurate predictions. With these results, this research affirms the importance of using the Random Forest algorithm in developing early detection systems for lung cancer. This not only can improve treatment success rates but also significantly reduce mortality rates from lung cancer.
Penerapan Metode Single Moving Average Untuk Memprediksi Harga Besi Pada Usaha Dagang Ragam Besi: Implementasi forecasting harga besi menggunakan metode single moving avarage di UD.Ragam Besi Levia, Zachdyna Aurelya; Barata, Mula Agung; Rohmah, Roihatur
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 6 (2025): JPTI - Juni 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.854

Abstract

Besi rongsok merupakan material logam bekas yang masih memiliki nilai ekonomi karena dapat didaur ulang. Namun, permasalahan yang dihadapi oleh pengusaha adalah kesulitan dalam meramalkan harga besi rongsok yang berfluktuasi. Penelitian ini bertujuan untuk meramalkan harga besi rongsok menggunakan metode Single Moving Average (SMA) guna membantu pengusaha dalam pengambilan keputusan harga jual. Data yang digunakan berasal dari UD Ragam Besi, mencakup harga besi dari Januari 2016 hingga Oktober 2024. Peramalan dilakukan dengan menggunakan periode 4 bulan terakhir. Evaluasi akurasi dilakukan menggunakan Mean Absolute Deviation (MAD), Mean Squared Error (MSE), dan Mean Absolute Percentage Error (MAPE). Hasil penelitian ini menunjukkan bahwa metode Single Moving Average dapat memberikan estimasi harga besi dengan tingkat akurasi yang tinggi, seperti yang tercermin dari hasil peramalan bulan Desember yang menunjukkan harga aktual sebesar 5.450, dengan nilai MAD sebesar 160,32, MSE sebesar 44.670,81, dan MAPE sebesar 2,77%. Nilai MAD dan MAPE yang relatif rendah menunjukkan bahwa tingkat kesalahan peramalan pada periode tersebut berada dalam batas wajar. Dengan demikian, metode ini dapat digunakan sebagai acuan untuk menetapkan strategi harga yang lebih optimal dan mengantisipasi fluktuasi harga besi rongsok di masa mendatang. Selain itu, kontibusi praktis dalam penelitian ini adalah memberikan metode peramalan yang dapat membantu pengusaha dalam mengambil keputusan penetapan harga jual berdasarkan peramalan yang akurat.
Optimization of Random Forest Algorithm with Backward Elimination Method in Classification of Academic Stress Levels Amalia, Salsabila Dani; Barata, Mula Agung; Yuwita, Pelangi Eka
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9280

Abstract

Stress is a phenomenon experienced by all individuals as a natural response to pressure, which can impact mental and physical health. In an academic setting, the stress experienced by students is known as academic stress, which can affect their performance and mental well-being. Therefore, there is a need for effective prediction methods to aid in the management and prevention of academic stress. Therefore, there is a need to predict the level of academic stress to aid more effective management and prevention. This study uses a public dataset categorized based on the Student-life Stress Inventory (SSI), which includes psychological, physiological, social, environmental, and academic factors. Data mining is often used to detect diseases, one of which is the Random Forest algorithm. The Random Forest algorithm is applied as a classification technique for academic stress levels, with optimization using the Backward Elimination method for feature selection to improve model accuracy. The results showed that the accuracy of the Random Forest algorithm without feature selection obtained an accuracy of 86%, compared to the random forest algorithm with feature selection using the Backward Elimination method obtained a higher accuracy of 88%. This increase shows that the feature selection method can optimize model performance by selecting more relevant features. Thus, this research is expected to contribute to the management of student academic stress against the risk of academic stress.
A Improving House Price Clustering Results with K-means through the Implementation of One-hot Encoding Pre-processing Technique Maulani, Vicka Rizqi; Barata, Mula Agung; Yuwita, Pelangi Eka
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9481

Abstract

Basic human needs include a house that serves as a place to live and a shelter from everything. In Indonesia, owning a house is still a challenging aspect due to its high price. Information on house prices is needed for prospective buyers or consumers, so that buyers can adjust their needs and finances, and for producers or sellers it is used as a way to determine the segmentation of targeted market groups. House prices are influenced by several factors including, building area, number of bedrooms, number of bathrooms, location, condition and the presence of a garage. This research aims to improve the quality of house price clustering with K-means and the application of one-hot encoding in the data pre-processing process in representing categorical data. The dataset used has two types of data, namely numeric and categorical. The cluster evaluation is based on the silhouette score matrix and the determination of k is based on the elbow graph. The results showed an increase in the silhouette score value after applying one-hot encoding 0.15 which was previously 0.09, with the number of k = 3. The 0.15 matrix result is relatively low, which is caused by the overlap of house price values in the dataset, but it has been shown that one-hot encoding can represent categorical data well in the data pre-processing process so that the data can be processed with the k-means algorithm.
Penerapan Data Mining pada Algoritma Multiple Linear Regression dalam Peramalan Harga Emas Dina, Intan Rachma; Barata, Mula Agung; Yuwita, Pelangi Eka
SMARTICS Journal Vol 11 No 1 (2025): SMARTICS Journal (April 2025)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v11i1.11710

Abstract

Gold, a precious metal, is highly favored for its ease of maintenance and low risk of loss, making it a popular investment choice. However, gold prices are subject to fluctuations influenced by factors such as the dollar exchange rate, market demand and supply, and monetary crises. Understanding these fluctuations is crucial for investors to minimize losses and maximize profits. The dataset, sourced from Investing.Com, spans from January 2019 to December 2024 and includes 1548 records with five attributes. The error rate was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). This study aims to forecast gold prices using the Multiple Linear Regression algorithm, with the K-Fold Cross Validation method applied to enhance model accuracy. The results show RMSE and MAPE values of 695.7909 and 0.27%, respectively, indicating that the Multiple Linear Regression algorithm is effective in predicting gold prices.
Klasifikasi Status Stunting Pada Balita di Kecamatan Singgahan dengan Algoritma Ningrum, Sinta; Barata, Mula Agung; Mahmudah, Nur
SMARTICS Journal Vol 11 No 1 (2025): SMARTICS Journal (April 2025)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v11i1.11862

Abstract

Stunting is a chronic health issue that significantly impacts the physical growth and cognitive development of children, particularly in developing countries such as Indonesia. This study aims to classify stunting status among toddlers in the Singgahan District by applying the Support Vector Machine (SVM) algorithm, optimized using Grid Search Cross-Validation. The dataset consists of 642 toddler records with nine attributes representing nutritional and growth conditions. The classification process involves several stages, including data preprocessing, handling data imbalance using the SMOTE method, and model performance evaluation through 5-fold cross-validation. The results show that the SVM algorithm without optimization achieved an accuracy of 69.83%, while optimization with Grid Search Cross-Validation significantly increased the accuracy to 93.33%. These findings indicate that the application of SVM with hyperparameter tuning via Grid Search Cross-Validation is effective in improving classification accuracy for stunting status in toddlers. This research contributes to the use of machine learning in supporting decision-making processes in public health sectors.
Sosialisasi DBD berbasis Statistik Kesehatan dan Agama di Pondok Pesantren Raudhatul Ulum Campurejo Bojonegoro Choiri, Moh. Miftahul; Nurdiansyah, Denny; Barata, Mula Agung; Abidin, Zainul; Nasirudin, M.
Jurnal SOLMA Vol. 14 No. 2 (2025)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/solma.v14i2.18091

Abstract

Background: The dengue prevention socialization program at Pondok Pesantren Raudhatul Ulum, Bojonegoro, was conducted to anticipate the spread of dengue fever during the rainy season. Factors such as population density, health promotion, and community attitudes influence dengue cases, while public knowledge has yet to show a significant impact. The program aimed to educate students on the importance of healthy living and maintaining environmental cleanliness to prevent dengue fever. Methods: The program was designed in four stages: socialization, technology implementation, mentoring and evaluation, and sustainability. The focus was on education, the use of simple tools, and enhancing the capacity of health cadres. Sustainability was ensured through integration into the boarding school’s routine agenda, active partner involvement, and collaboration with health institutions. Results: The dengue prevention socialization program at Pondok Pesantren Raudhatul Ulum, Bojonegoro, on November 11, 2024, utilized a Participatory Learning and Action (PLA) approach that actively engaged students. The activities included educating the students on the 4M Plus method, distributing guidebooks, and providing larvicide. The main challenge was ensuring students' consistency in implementing preventive measures, while sustainability opportunities were supported through regular monitoring. Conclusions: The dengue prevention socialization program at Pondok Pesantren Raudhatul Ulum successfully increased students' knowledge and participation through the 4M Plus method and an interactive approach, although consistent implementation of preventive measures still requires further monitoring.
Optimization of Random Forest Algorithm with SMOTE Method to Improve the Accuracy of Early Diabetes Prediction Nisa, Siti Khoirun; Barata, Mula Agung; Yuwita, Pelangi Eka
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.22986

Abstract

Purpose: This research aims to examine the performance of the random forest algorithm in diabetes risk classification with data balancing using the Synthetic Minority Oversampling Technique (SMOTE) method to improve the representation of minority classes and increase the prediction accuracy value. Methods: The study used the Behavioral Risk Factor Surveillance System (BRFSS) dataset, obtained from Kaggle, which contains health-related survey data used to identify individuals at risk of diabetes. The Random Forest algorithm was applied to classify diabetes. To balance the data, the SMOTE method was used. The model’s performance was evaluated using 10-fold cross-validation by comparing result before and after SMOTE. Result: The results showed that the application of the SMOTE method improved the performance of the Random Forest classification model, especially in minority classes. Model performance in minority classes without SMOTE had poor evaluation metrics with precision of 49%, recall of 18%, and F1-score of 26%. After applying SMOTE, these values increased to precision of 96%, recall of 88%, and F1-score of 92%. Representing improvements of 47 percentage points in precision, 70 points in recall, and 66 points F1-score. The overall accuracy of the Random Forest model also increased from 86% to 92%, showing a 6 percentage point improvement. Novelty: This study use integrating the Random Forest algorithm with the SMOTE technique and validating the results using 10-fold cross-validation. The combination significantly improves minority class prediction performance in early diabetes detection, addressing the common limitations of previous studies in handling imbalanced datasets effectively.
Klasifikasi Stunting Pada Balita dengan Algoritma Random forest dan Support Vector machine Panigoro, Buyung; Barata, Mula Agung; Mahmudah, Nur
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.
Co-Authors Abdul Aziz Amalia, Salsabila Dani Andiyani, Putri Aprillia Dwi Ardianti Deni Reskianto Deni Denny Nurdiansyah Dina, Intan Rachma Dwi Irnawati Dwi Syafi'i, Ahmad Dwi Tiyas Novitasari Edi Noersasongko Efendi, Ervina Putri Fina Indri Silfana Hidayah, Alvinatul Ilmiyah, Miftakhul Indra Dharma Wijaya Indra Dharma Wijaya, Indra Dharma Ita Aristia Sa'ida Jauhar Vikri, Muhammad Laili, Elok Fathiyatul Levia, Zachdyna Aurelya Maulani, Vicka Rizqi Moch Arief Soeleman Moh. Miftahul Choiri Moh. Muhajir Munir, Ach Sirojul Muzakka, Moch. Arifuddin Nasirudin, M. Nisa, Siti Khoirun Novitasari, Dwi Tiyas Nur Mahmudah Nur’aini, Zulfiana Panigoro, Buyung Pelangi Eka Yuwita Pratiwi, Fannisa Salsabila Purwanto Purwanto Reza Anggapratama Rheyna Anggri Setyani Rochmatin, Novia Nur Roihatur Rohmah Sahri Sahri Sahri Santi, Nirma Ceisa Saputra, Agus Bima Silfana, Fina Indri Sinta Ningrum Usman Nurhasan Vita Dwi Rahmawati Wulan, Diah Nawang Yaqin, Ahmad Ainul Zainul Abidin Zakki Alawi