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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
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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.
Analisis Perbandingan Seleksi Fitur dalam Memprediksi Kelulusan Mahasiswa dengan Menngunakan Artificial Neural Network M. Khoirul Risqi; Dwi Prastya, Ifnu Wisma; Barata, Mula Agung
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.v13i1.9420

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
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.v13i1.9527

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.
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.
Komparasi Algoritma Machine Learning untuk Deteksi Review Palsu dan Rekomendasi Pembelian Pada Platform Lazada Prabowo, Affan Agung; Barata, Mula Agung; Sa'ida, Ita Aristia
VOCATECH: Vocational Education and Technology Journal Vol 8, No 1 (2026): April
Publisher : Akademi Komunitas Negeri Aceh Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38038/vocatech.v8i1.307

Abstract

AbstractThe rapid growth of e-commerce has increased the potential for the emergence of fake reviews that can mislead consumers and reduce the credibility of online purchasing decisions. This study aims to evaluate the performance of several machine learning algorithms in distinguishing fake and genuine reviews, as well as to develop a purchase recommendation model that considers review authenticity. The dataset used consists of 2,644 product reviews from the Lazada platform, which were labeled using a rule-based approach, followed by text preprocessing, normalization, and feature extraction using TF-IDF. The classification methods applied include Support Vector Machine (SVM), Decision Tree, Random Forest, Naive Bayes, and C4.5. The results show that Random Forest and C4.5 achieved the highest accuracy of 99.81%, followed by Decision Tree (99.62%), SVM (98.30%), and Naive Bayes (93.01%). In addition, a purchase recommendation score was developed by combining rating, sentiment, helpfulness, and purchase status to classify products into recommended and not recommended categories. The findings indicate that most reviews identified as fake still result in positive recommendations, which may introduce bias in conventional recommendation systems. Therefore, integrating fake review detection with sentiment analysis and multi-criteria evaluation is essential to improve the reliability of recommendation systems in e-commerce platforms. AbstrakMaraknya perkembangan e-commerce meningkatkan potensi munculnya ulasan palsu yang dapat menyesatkan konsumen dan menurunkan kredibilitas dalam pengambilan keputusan pembelian secara daring. Penelitian ini bertujuan untuk mengevaluasi kinerja beberapa algoritma machine learning dalam membedakan ulasan palsu dan asli, serta mengembangkan model rekomendasi pembelian yang mempertimbangkan keaslian ulasan. Dataset yang digunakan terdiri dari 2.644 ulasan produk pada platform Lazada yang diberi label menggunakan pendekatan rule-based, kemudian melalui tahapan preprocessing teks, normalisasi, dan ekstraksi fitur menggunakan TF-IDF. Metode klasifikasi yang diterapkan meliputi Support Vector Machine, Decision Tree, Random Forest, Naive Bayes, dan C4.5. Hasil pengujian menunjukkan bahwa Random Forest dan C4.5 mencapai akurasi tertinggi sebesar 99,81%, diikuti oleh Decision Tree (99,62%), SVM (98,30%), dan Naive Bayes (93,01%). Selain itu, dikembangkan skor rekomendasi pembelian dengan menggabungkan rating, sentimen, tingkat helpful, dan status pembelian untuk mengelompokkan produk ke dalam kategori direkomendasikan dan tidak direkomendasikan. Temuan menunjukkan bahwa sebagian besar ulasan yang terdeteksi sebagai palsu tetap menghasilkan rekomendasi positif, sehingga berpotensi menimbulkan bias pada sistem rekomendasi konvensional. Oleh karena itu, integrasi deteksi ulasan palsu dengan analisis sentimen serta penilaian multi-kriteria menjadi penting untuk meningkatkan keandalan sistem rekomendasi pada platform e-commerce.  
Analisis Klasifikasi Hepatitis Menggunakan Synthetic Minority Oversampling Technique, Support Vector Machine, dan Random Forest Laily, Amalia Nur; Barata, Mula Agung; Nurdiansyah, Denny
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 6 No. 1: MARET 2026
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v6i1.1630

Abstract

Hepatitis akibat infeksi virus masih menjadi masalah kesehatan masyarakat yang serius sehingga deteksi dini berbasis data klinis penting untuk mencegah kerusakan hati lebih lanjut. Penelitian ini menganalisis kinerja algoritma Support Vector Machine (SVM) dan Random Forest pada klasifikasi hepatitis serta mengkaji dampak penerapan Synthetic Minority Over-sampling Technique (SMOTE). Dataset yang digunakan adalah HepatitisCdata.csv dari Kaggle dengan 615 data pasien yang memuat atribut demografis dan parameter biokimia hati. Tahapan penelitian meliputi preprocessing data, penanganan outlier, transformasi atribut kategorikal, serta pembangunan model baseline dan SMOTE. Evaluasi dilakukan menggunakan 10-fold cross-validation dengan metrik akurasi, presisi, recall, dan F1-score. Hasil menunjukkan bahwa SMOTE meningkatkan performa kedua algoritma, dengan Random Forest + SMOTE memberikan hasil terbaik (akurasi 98,85%) dibandingkan SVM + SMOTE (98,50%). Kontribusi penelitian ini terletak pada penggunaan pipeline preprocessing dan evaluasi yang seragam untuk membandingkan dampak SMOTE secara langsung pada dua algoritma klasifikasi hepatitis.
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)

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Co-Authors Abdul Aziz Alfianto Faidatul Aldi Yumardiansyah Amalia, Salsabila Dani Andiyani, Putri Aprillia Dwi Ardianti Deni Reskianto Deni Denny Nurdiansyah Dina Selvi Rahmadani 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 Eka Wahyu Andriyani Elok Fathiyatul Laili 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 Jauhar Vikri, Muhammad Laily, Amalia Nur Lambang, Rahmat Tegar Patriot Hari Levia, Zachdyna Aurelya 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 Nasirudin, M. Nisa, Siti Khoirun Novitasari, Dwi Tiyas Nur Mahmudah Nur’aini, Zulfiana Panigoro, Buyung Pelangi Eka Yuwita Prabowo, Affan Agung Pradema Sanjaya, Ucta Prastya, Ifnu Wisma Dwi Purwanto Purwanto Putri Amelia 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 Shofiatuz Zulfia Silfana, Fina Indri Sinta Ningrum Taufik Hidayat Teguh Pribadi Usman Nurhasan Viki Mei Adi Saputra Vita Dwi Rahmawati Wisma Dwi Prastya, Ifnu Wulan, Diah Nawang Yaqin, Ahmad Ainul Zainul Abidin Zakki Alawi