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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 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) 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 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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IMPLEMENTASI ALGORITMA MULTIPLE LINEAR REGRESSION DALAM MENGESTIMASI HASIL PANEN TANAMAN TEMBAKAU wulan, Diah nawang; Barata, Mula Agung; Sa'ida, Ita Aristia
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3662

Abstract

Penilitian ini bertujuan untuk menaksir panen tembakau petani di Desa Balongrejo menggunakan algoritma regresi linier khusus. Data yang digunakan terdiri dari empat variabel dasar: jumlah bit, jumlah pembelian, jumlah transaksi, dan jumlah jam. Analisis dilakukan secara manual dan dengan bantuan alat statistik. Hasil analisis data menunjukkan bahwa model regresi dapat menjelaskan 95,5% varians dalam data. Selain itu, uji F menunjukkan semua variabel memiliki pengaruh yang signifikan secara bersamaan, sedangkan uji t mengidentifikasi tiga variabel yang memiliki pengaruh signifikan secara terpisah.
KOMPARASI METODE SVM DAN C4.5 DENGAN BACKWARD ELIMINATION UNTUK KLASIFIKASI STRES Efendi, Ervina Putri; Barata, Mula Agung; Ardianti, Aprillia Dwi
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 3 (2025): Oktober 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i3.2130

Abstract

Kesehatan mental mahasiswa menjadi perhatian penting dalam dunia pendidikan, terutama terkait tingkat stres yang dialami selama proses akademik yang dapat berdampak pada prestasi dan kesejahteraan mereka. Penelitian ini bertujuan untuk membandingkan kinerja algoritma Support Vector Machine (SVM) dan C4.5 dalam mengklasifikasikan tingkat stres mahasiswa guna menentukan metode yang lebih optimal. Penelitian ini menggunakan dataset dari Kaggle yang berisi 1.100 data mahasiswa dengan 20 atribut penyebab stres. Data tersebut diproses melalui tahapan normalisasi menggunakan MinMax Scaling dan seleksi fitur dengan metode Backward Elimination untuk mengoptimalkan model. Klasifikasi tingkat stres dibagi ke dalam tiga kategori: ringan, sedang, dan berat. Evaluasi model dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil analisis menunjukkan bahwa algoritma SVM memberikan akurasi tertinggi sebesar 91% dengan nilai presisi, recall, dan F1-score yang konsisten, sementara algoritma C4.5 menghasilkan akurasi 90% dengan hasil evaluasi yang serupa. Temuan ini menegaskan bahwa SVM lebih unggul dalam mengklasifikasikan tingkat stres mahasiswa dibandingkan C4.5. Kesimpulan dari penelitian ini adalah bahwa penerapan machine learning, khususnya SVM, dapat menjadi pendekatan efektif untuk deteksi dini tingkat stres mahasiswa dan berpotensi digunakan sebagai dasar pengembangan sistem pendukung keputusan dalam upaya pencegahan masalah kesehatan mental di lingkungan pendidikan.
Peramalan Penjualan Obat dengan Menggunakan Metode Single Moving Average Hidayah, Alvinatul; Barata, Mula Agung; Ardianti, Aprillia Dwi
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.
Implementation of ANN Optimization with SMOTE and Backward Elimination for PCOS Prediction Ilmiyah, Miftakhul; Barata, Mula Agung; Yuwita, Pelangi Eka
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.22886

Abstract

by women, making it potentially fatal owing to delayed diagnosis and treatment. With the advent of current technology, machine learning and medical care may become associated with disease prediction. The purpose of the study is to predict PCOS using an Artificial Neural Network (ANN) Deep Learning algorithm combined with Synthetic Minority Oversampling Technique (SMOTE) for data balancing and backward elimination for feature selection, aiming to provide a more accurate diagnosis of PCOS with high accuracy from thoose combination. Methods: ANN algorithm structure with three hidden layers, each with a ReLU activation function of 128, 64, and 32 neurons, a Dropout layer, an output layer with a sigmoid activation function, and an Adam learning rate. Result: Using the SMOTE approach for data balance and backward elimination feature selection, the research attributes are reduced to 18. And ANN algorithm predicts PCOS disease achieve an accuracy of 92%. Novelty: This study uses an ANN algorithm model combined with the SMOTE data balancing technique and a feature selection method using backward elimination. These methods and techniques have proven to have high accuracy. The results of this study are expected to be used as a more accurate diagnosis by medical professionals in predicting PCOS disease.
Using K-NN Algorithm for Evaluating Feature Selection on High Dimensional Datasets Silfana, Fina Indri; Barata, Mula Agung
JURNAL TEKNIK INFORMATIKA Vol. 17 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i2.40866

Abstract

Data mining is the process of using statistics, mathematics, artificial intelligence and machine learning to identify problems that exist in data so as to produce useful information. Based on its function, data mining is grouped into description, estimation, classification, clustering, and association. K-NN is one of the best data mining methods and is widely used in research. K-NN algorithm was introduced by Fix and Hodges in 1951. K-NN algorithm is a simple algorithm and is often used to cluster supervised data. Feature selection attribute selection is a data mining technique used in the pre-processing stage. This technique works by reducing complex attributes that will be managed at the processing and analysis stage. In this study, the most effective feature selection to improve the accuracy of the K-NN algorithm by increasing accuracy by 95.12% on the breast cancer dataset and 88.75% on the prostate cancer dataset.
ANALISIS STRATEGI GREEN MARKETING, STORE ATMOSPHERE, DAN BRAND AMBASADOR TERHADAP MINAT BELI PELANGGAN THE BODY SHOP DI GRAND CITY SURABAYA Irnawati, Dwi; Barata, Mula Agung
Jurnal Review Pendidikan dan Pengajaran Vol. 7 No. 3 (2024): Vol. 7 No. 3 (2024): Volume 7 No 3 Tahun 2024 (Special Issue)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jrpp.v7i3.31487

Abstract

Penelitian ini memiliki tujuan untuk menganalisis strategi Green Marketing, Store Atmosphere, dan Brand Ambassador Terhadap Minat Beli pelanggan The Body Shop di Grand City Surabaya. Penggunaan metode dalam penelitian ini adalah metode kuantitatif dan hasil penelitian didasarkan pada jawaban responden dengan menggunakan skala Likert 1-5. Penelitian ini menggunakan populasi  pelanggan The Body Shop di Grand City Surabaya, dan sampel yang digunakan dalam penelitian ini berjumlah 150 responden. A Store Atmosphere Store Atmosphere nalisis data yang digunakan dalam penelitian ini adalah Uji Regresi Linier Berganda, Uji f, Uji t dan Koefisien determinasi (R2). Hasil penelitian menunjukkan bahwa secara simultan (uji f) menunjukkan terdapat pengaruh yang signifikan antara variabel Green Marketing (X1), Store Atmosphere (X2) dan Brand Ambassador (X3) terhadap Minat Beli Pelanggan (Y). secara parsial (Uji t) Brand Ambassador tidak berpengaruh signifikan terhadap Minat Beli pelanggan dan Store Atmosphere juga tidak berpengaruh signifikan terhadap Minat Beli pelanggan, sedangkan Green Marketing berpengaruh signifikan terhadap Minat Beli pelanggan.
Perbandingan Akurasi Algoritma Naive Bayes dan Algoritma Decision Tree dalam Pengklasifikasian Penyakit Kanker Payudara Munir, Ach Sirojul; Saputra, Agus Bima; Aziz, Abdul; Barata, Mula Agung
Jurnal Ilmiah Informatika Global Vol. 15 No. 1: April 2024
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v15i1.3578

Abstract

Cancer is one of the deadliest diseases in the world with a high increase in the number of cases every year Cancer disease with significant growth in cases, is a serious global challenge. The main focus of this research is breast cancer in Indonesia. Using a data mining approach, this study compares two main classification algorithms, namely Naive Bayes and Decision Tree, to identify breast cancer. Naive Bayes is a simple probabilistic approach, calculating probabilities assuming attribute independence. Decision Tree, as a popular algorithm, represents decision rules in the form of a tree. Through comparison with previous research on algorithms in other contexts, this study aims to find the algorithm with the highest accuracy in breast cancer classification. With the final result, the decision tree has a higher accuracy of 92.04% and naïve Bayes has an accuracy of 91.15%.This result proves that the decision tree is superior in the classification of breast cancer disease compared to naïve Bayes. The results of the study are expected to make an important contribution to the development of effective approaches for the diagnosis and treatment of breast cancer.
IMPLEMENTASI METODE SMOTE DAN RANDOM OVER-SAMPLING PADA ALGORITMA MACHINE LEARNING UNTUK PREDIKSI CUSTOMER CHURN DI SEKTOR PERBANKAN Fannisa Salsabila Pratiwi; Mula Agung Barata; Aprillia Dwi Ardianti
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 1 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i1.3678

Abstract

The ability to anticipate unsubscribed customers is a challenge in the competitive banking industry, where it is more efficient to retain customers than to attract new ones. The purpose of this study is to improve the effectiveness of churn prediction by overcoming data imbalances using SMOTE (Synthetic Minority Oversampling Technique) and Random Over-sampling. The data set used consists of 10. 000 bank customer data, with 12 important attributes, including churn indicators as targets. The machine learning algorithms used are Random Forest and Neive Bayes, evaluated based on accuracy, precision, recall, and F1 scores. The results of the experiment showed that the highest accuracy of 87.13% could be achieved with the Random Forest algorithm without using the oversampling method, but its effectiveness in detecting churn customers was slightly limited. The use of SMOTE and Random Over-sampling methods has improved the model's performance in identifying churn patterns, although it has led to a decrease in accuracy to 86.20% for Random Over-sampling and 81.47% for SMOTE. Nevertheless, the Neive Bayes algorithm showed the best accuracy rate of 79.20% without oversampling, although it was still slightly lacking in optimal churn handling. The study underscores the importance of using oversampling methods to improve prediction balance in minority classes, which is often overlooked in conventional models. It is hoped that the results of this research can be used as a guide in improving strategies to maintain customer trust that are more up-to-date and efficient.
KOMPARASI ALGORITMA DECISION TREE DAN SUPPORT VECTOR MACHINE (SVM) DALAM KLASIFIKASI SERANGAN JANTUNG Elok Fathiyatul Laili; Zakki Alawi; Roihatur Rohmah; Mula Agung Barata
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 1 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i1.3683

Abstract

The heart is one of the most important organs in the human body. According to the WHO, heart attacks are the most common cause of sudden death worldwide, with more than 17.8 million people dying from heart attacks. A heart attack occurs when blood flow to the coronary arteries stops, depriving the heart muscle of oxygen, and causing a heart attack. Detecting a heart attack is very difficult due to the various symptoms. The purpose of this research is to compare the performance of the accuracy values of two algorithms, namely Decision Tree and Support Vector Machine (SVM) in classifying heart attacks. The results of this study show that the Decision Tree algorithm achieves the highest accuracy results compared to the SVM algorithm. The accuracy of the Decision Tree algorithm using a 60:40 ratio data splitting is 98.11% with a negative precision of 98.01% and positive of 98.17% and a negative recall of 97.04% and positive of 98.77%. Meanwhile, the SVM algorithm using data splitting with the same ratio produces an accuracy value of 92.80% with a negative precision of 90.24% and a positive of 94.43% and a negative recall of 91.13% and a positive of 93.85%.
Perbandingan Algoritma Machine Learning untuk Klasifikasi Kopi Menggunakan Data Sensor Electronic Nose dan Tongue Dwi Issadari Hastuti; Mula Agung Barata; Ifnu Wisma Dwi Prastya
JURNAL RISET KOMPUTER (JURIKOM) 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.
Co-Authors Abdul Aziz Amalia, Salsabila Dani Amelia, Putri Andiyani, Putri Andriyani, Eka Wahyu Aprillia Dwi Ardianti Deni Reskianto Deni Denny Nurdiansyah Denny Nurdiansyah Dina, Intan Rachma Distira, Riski Putra Ayu Dwi Irnawati Dwi Issadari Hastuti Dwi Prastya, Ifnu Wisma Dwi Syafi'i, Ahmad Dwi Tiyas Novitasari Edi Noersasongko Efendi, Ervina Putri Elok Fathiyatul Laili Fannisa Salsabila Pratiwi Fina Indri Silfana Hidayah, Alvinatul Ifnu Wisma Dwi Prastya Ilmiyah, Miftakhul Indra Dharma Wijaya Indra Dharma Wijaya, Indra Dharma Ita Aristia Sa'ida Jauhar Vikri, Muhammad Levia, Zachdyna Aurelya M. Khoirul Risqi Maulani, Vicka Rizqi Moch Arief Soeleman Moh. Miftahul Choiri Moh. Yusuf Efendi Munir, Ach Sirojul Muzakka, Moch. Arifuddin Nasirudin, M. Nisa, Siti Khoirun Novitasari, Dwi Tiyas Nur Mahmudah Nur’aini, Zulfiana Panigoro, Buyung Pelangi Eka Yuwita Purwanto Purwanto Rahmadani, Dina Selvi Reza Anggapratama Rheyna Anggri Setyani Rochmatin, Novia Nur Roihatur Rohmah Roihatur Rohmah Sahri Sahri Sahri Santi, Nirma Ceisa Saputra, Agus Bima Shafa Kirana Aralia Shofiatuz Zulfia Silfana, Fina Indri Sinta Ningrum Usman Nurhasan Viki Mei Adi Saputra Vita Dwi Rahmawati Wulan, Diah Nawang Yaqin, Ahmad Ainul Zainul Abidin Zakki Alawi