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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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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.
Rice Quality Identification Built on Indonesian Food Standards Based on Electronic Nose using Naïve Bayes Algorithm Jauhar Vikri, Muhammad; Wisma Dwi Prastya, Ifnu; Pradema Sanjaya, Ucta; Agung Barata, Mula
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.
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.
Indonesian Gold Price Forecasting Using Simple and Stacked LSTM with Expanding Window Lambang, Rahmat Tegar Patriot Hari; Prastya, Ifnu Wisma Dwi; Barata, Mula Agung Barata
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12148

Abstract

This study investigates the performance of two deep learning architectures, namely Simple LSTM and Stacked LSTM, for Indonesian gold price forecasting, with a particular focus on evaluating the effect of optimizer selection and learning rate configurations. An experimental framework is implemented using daily Indonesian gold price data from 2021 to 2024. Model performance is assessed using five-fold expanding window time series cross-validation to ensure robustness and avoid data leakage. Four adaptive training optimizers (Adam, Nadam, Adamax, and RMSprop) are evaluated across three learning-rate settings as part of a systematic sensitivity analysis of training hyperparameters. The results indicate that the Simple LSTM consistently outperforms the Stacked LSTM. The best performance is achieved by the Simple LSTM using the Adam optimizer with a learning rate of 0.01, yielding an RMSE of 9.235, MAE of 7.060, and MAPE of 0.71%. These findings demonstrate that simpler architectures combined with appropriate training configurations can provide superior forecasting accuracy for volatile financial time series.
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%.
Analisis Metode Ensemble Berbasis Random Forest untuk Klasifikasi Kejadian Stroke pada Dataset Publik Viki Mei Adi Saputra; Mula Agung Barata; Denny Nurdiansyah
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 3 (2026): Maret 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i3.9496

Abstract

Stroke merupakan salah satu penyebab utama disabilitas dan kematian global, sehingga diperlukan pendekatan berbasis data untuk mendukung klasifikasi kejadian stroke secara sistematis. Penelitian ini menganalisis variasi metode ensemble berbasis Random Forest pada dataset publik healthcare-dataset-stroke-data dari Kaggle yang terdiri dari 5.110 data pasien dengan 11 variabel demografis dan faktor risiko kardiovaskular. Tahapan prapemrosesan meliputi imputasi nilai hilang pada atribut bmi menggunakan median, penanganan outlier dengan metode interquartile range (IQR), serta penyeimbangan kelas menggunakan SMOTE. Tiga skenario model dikembangkan dalam satu pipeline yang seragam, yaitu Random Forest sebagai baseline, Bagging Random Forest, dan AdaBoost Random Forest. Evaluasi dilakukan menggunakan 5-Fold Cross Validation dengan metrik akurasi, presisi, recall, dan F1-score. Hasil analisis menunjukkan adanya perbedaan nilai metrik evaluasi antar skema ensemble, dengan konfigurasi AdaBoost Random Forest menghasilkan nilai akurasi sebesar 94,70% pada konfigurasi pengujian yang digunakan. Studi ini memfokuskan analisis pada variasi strategi ensemble dalam satu kerangka Random Forest dengan pipeline prapemrosesan yang seragam, sehingga menghasilkan evaluasi yang terkontrol dan reprodusibel.
Evaluasi Komparatif Metode Feature Selection pada XGBoost Regression untuk Prediksi Panjang Siklus Menstruasi Shofiatuz Zulfia; Mula Agung Barata; Ifnu Wisma Dwi Prastya
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 3 (2026): Maret 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i3.9526

Abstract

Panjang siklus menstruasi menjadi indikator utama dalam kesehatan reproduksi perempuan, namun perbedaan karakteristik individu dan ketidakteraturan siklus menyulitkan proses prediksi secara manual. Kondisi tersebut mendorong perlunya pendekatan berbasis data yang mampu menghasilkan prediksi panjang siklus menstruasi secara akurat dan konsisten. Penelitian ini bertujuan untuk melakukan evaluasi komparatif berbagai metode feature selection pada algoritma XGBoost Regression dalam memprediksi panjang siklus menstruasi. Dataset penelitian diperoleh dari Kaggle dan terdiri atas 162 data yang mencakup atribut fisiologis dan demografis perempuan. Tahapan penelitian meliputi preprocessing data, normalisasi menggunakan StandardScaler, pembagian data latih dan data uji dengan rasio 80:20, serta validasi 10-fold cross-validation untuk menguji stabilitas model. Empat skenario pemodelan dievaluasi, yaitu tanpa feature selection sebagai baseline, forward selection, backward elimination, dan optimized selection berbasis ensemble feature selection dari lima metode seleksi fitur. Hasil evaluasi menunjukkan bahwa metode forward selection memberikan performa terbaik dengan nilai R² sebesar 0,9005, RMSE 1,45 hari, MAE 0,57 hari, dan MAPE 1,73% (kesalahan relatif rata-rata < 2% terhadap panjang siklus 25-30 hari), serta meningkatkan nilai R² sebesar 0,1696 poin (dari 0,7309 menjadi 0,9005), setara dengan peningkatan relatif 23,2% terhadap nilai baseline. Temuan ini menunjukkan bahwa pemilihan metode feature selection yang tepat berpengaruh terhadap peningkatan performa prediktif dan stabilitas model XGBoost Regression dalam prediksi panjang siklus menstruasi.
Pemberdayaan Guru BK dalam Meningkatkan Kematangan Karir Siswa melalui pelatihan Modul Bimbingan Karir Interaktif Berbasis Multimedia dan Nilai-Nilai Islam Moh. Yusuf Efendi; Riski Putra Ayu Distira; Mula Agung Barata; Dina Selvi Rahmadani; Putri Amelia; Eka Wahyu Andriyani
Jurnal SOLMA Vol. 15 No. 1 (2026)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/solma.v15i1.21403

Abstract

Background: Penggunaan media berbasis teknologi dalam bimbingan karier di Sekolah Menengah Kejuruan (SMK) menjadi penting seiring transformasi digital di dunia pendidikan dan kerja. Perubahan cepat kebutuhan industri menuntut siswa memiliki pemahaman karier yang relevan dan adaptif terhadap teknologi. Pemanfaatan media digital seperti platform asesmen karier online, video interaktif, dan e-modul membantu guru Bimbingan dan Konseling (BK) memberikan layanan karier yang lebih menarik dan interaktif. Karena itu, kapasitas guru BK perlu diperkuat melalui program pemberdayaan berbasis peningkatan kompetensi pedagogis dan teknologis. Metode pengabdian menggunakan Participatory Learning and Action melalui lima tahapan: sosialisasi, pelatihan, penerapan teknologi, pendampingan, dan evaluasi keberlanjutan. Hasil kegiatan menunjukkan peningkatan kemampuan guru BK dalam penguasaan teknologi pembelajaran sebesar 32%. Sebanyak 78% siswa menyatakan layanan bimbingan karier melalui modul interaktif lebih menarik dan mudah dipahami. Seluruh guru BK (100%) terlibat aktif, satu modul digital berbasis nilai Islam berhasil dikembangkan, dan dua sesi layanan nyata mendapat tanggapan positif dari lebih 75% siswa. Kesimpulan, kegiatan ini terbukti efektif meningkatkan kompetensi digital dan profesionalisme guru BK, memperkuat nilai-nilai Islam dalam layanan, serta menumbuhkan partisipasi aktif siswa dalam perencanaan karier. Selain itu, terbentuk Komunitas Praktisi Guru BK SMKN 5 Bojonegoro sebagai wadah inovasi berkelanjutan dalam pengembangan layanan BK berbasis teknologi dan spiritualitas Islam.
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&#039;ida Ita Aristia Sa&#039;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