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All Journal Riau Journal of Computer Science JURNAL INSTEK (Informatika Sains dan Teknologi) JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Building of Informatics, Technology and Science Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Computer Science and Information Technology (CoSciTech) Jutsi: Jurnal Teknologi dan Sistem Informasi J-Com (Journal of Computer) Jurnal Ipteks Terapan : research of applied science and education Jurnal Komtekinfo Jurnal Bangun Abdimas Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Journal Of Indonesian Social Society Jurnal Pengabdian Harapan Bangsa Jurnal Teknologi Ilmu Komputer The Indonesian Journal of Computer Science Journal Computer Science and Applied Technology
Rolly Yesputra
Sekolah Tinggi Manajemen Informatika Dan Komputer Royal
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Digital Camp for Student : Workshop Pemrograman Java untuk Siswa SMK Negeri Air Joman Jurusan Rekayasa Perangkat Lunak Rolly Yesputra; Edi Kurniawan; Nasrun Marpaung; Nuriadi Manurung
Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Vol. 1 No. 2 (2021): Desember 2021
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jpstm.v1i2.822

Abstract

Abstract: The 4.0 Industrial Revolution can be seen directly from the very rapid progress and changes in information technology in various fields. To be able to keep up with technological developments in the era of the Industrial Revolution 4.0, SMK students must be able to follow the development of information technology, especially programming. With the motto "SMK Can" it is necessary to hold training activities to improve students' abilities. Java programming training is given to students majoring in Software Engineering. The training activity begins with the preparation of Java programming modules and practical activities with the Netbeans IDE to develop applications based on Java Swing and the MariaDB database. This training is carried out in the form of tutorials and workshops that are accompanied directly by the team, so that students can practice the material provided directly and are assisted if there are obstacles in following the modules discussed. The training always begins with a pre-test activity and at the end of the training a post-test will be conducted to test students' understanding. From the results of the pre-test, it is known that the average student does not understand Java programming well. After doing the Post-test, it was found that there was an increase in students' ability to understand Java Swing programming.            Keywords: Java; Java Swing; Programming  Abstrak: Revolusi Industri 4.0 dapat dilihat secara langsung dari kemajuan dan perubahan teknologi informasi yang sangat pesat di berbagai bidang. Untuk dapat mengikuti perkembangan teknologi di era Revolusi Industri 4.0, Siswa SMK harus mampu mengikuti perkembangan teknologi informasi khususnya pemrograman. Dengan motto “SMK Bisa” maka perlu diadakan kegiatan pelatihan untuk meningkatkan kemampuan siswa. Pelatihan pemrograman Java diberikan kepada Mahasiswa jurusan Rekayasa Perangkat Lunak. Kegiatan pelatihan diawali dengan penyusunan modul pemrograman Java dan kegiatan praktikum dengan Netbeans IDE untuk mengembangkan aplikasi berbasis Java Swing dan database MariaDB. Pelatihan ini dilaksanakan dalam bentuk tutorial dan workshop yang didampingi langsung oleh tim, sehingga mahasiswa dapat mempraktekkan materi yang diberikan secara langsung dan dibantu jika ada kendala dalam mengikuti modul yang dibahas. Pelatihan selalu diawali dengan kegiatan pre-test dan di akhir pelatihan akan dilakukan post-test untuk menguji pemahaman siswa. Dari hasil pre-test diketahui bahwa rata-rata siswa belum memahami pemrograman Java dengan baik. Setelah dilakukan Post-test ditemukan adanya peningkatan kemampuan siswa dalam memahami pemrograman Java Swing. Kata kunci: Java; Java Swing; Pemrograman;   
PERANCANGAN E-CRM PADA HAPPY PET CLINIC Meri Ulfa Sari; Rolly Yesputra; Elly Rahayu
Journal Computer Science and Applied Technology Vol. 1 No. 1 (2023): J-CSAT - Februari
Publisher : PT. Cahaya Pena Tech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67401/j-csat.v1i1.2

Abstract

enelitian ini bertujuan merancang dan mengimplementasikan sistem Electronic Customer Relationship Management (E-CRM) pada Happy Pet Clinic untuk meningkatkan serta mempertahankan kualitas pelayanan pelanggan. Klinik hewan ini berlokasi di Jalan Hayam Wuruk, Kota Jambi, dan menyediakan berbagai layanan seperti poliklinik, bedah, radiologi, laboratorium, vaksinasi, rawat inap, serta perawatan hewan. Permasalahan yang dihadapi meliputi belum optimalnya pelayanan dan kurangnya media informasi terkait layanan maupun promosi, seperti diskon dan program khusus. Selain itu, belum tersedia fasilitas untuk menampung saran, kritik, dan pertanyaan pelanggan. Sistem E-CRM dikembangkan menggunakan PHP dan MySQL. Hasilnya adalah aplikasi berbasis web yang mampu membantu promosi layanan, meningkatkan komunikasi, serta memudahkan pelanggan dalam melakukan pemesanan layanan dan produk
STACKING ENSEMBLE MODEL MACHINE LEARNING DETEKSI DINI RISIKO KESEHATAN MENTAL DI LINGKUNGAN PENDIDIKAN Lia Umbari Putri; Rolly Yesputra; Satria Yudha Prayogi; Nasrun Marpaung
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.v8i3.4147

Abstract

Abstract: Mental health issues such as depression are prevalent among students and significantly impact both academic performance and psychological well-being. While machine learning techniques have been widely employed to predict mental health conditions, single-model approaches often suffer from limited generalizability and interpretability. This study proposes a Stacked Ensemble Learning framework that integrates three heterogeneous base classifiers—Logistic Regression (LR), Support Vector Machine (SVM) with RBF kernel, and Random Forest (RF)—combined with a meta-learner to enhance the accuracy and robustness of depression prediction among students. Experiments were conducted on a large-scale student mental health dataset comprising 27,901 records, with preprocessing steps including feature standardization, class balancing using SMOTE, and stratified cross-validation. Performance evaluation utilized Confusion Matrix, F1-Score, Recall, Precision, and the Area Under the ROC Curve (AUC-ROC). The proposed ensemble model achieved a classification accuracy of 84%, an AUC of 0.911, and an average precision of 0.89, consistently outperforming individual baseline classifiers. These results validate that combining margin-based, non-linear, and tree-based models can yield more reliable and interpretable predictions. The proposed architecture presents a promising and explainable tool for early detection of mental health issues within educational settings. Keywords: depression detection; student mental health; ensemble learning;machine learning. Abstrak: Permasalahan kesehatan mental seperti depresi kerap dialami oleh siswa dan berdampak pada performa akademik serta kesejahteraan psikologis mereka. Meskipun pendekatan pembelajaran mesin telah banyak digunakan untuk prediksi kondisi ini, model tunggal kerap menghadapi keterbatasan dalam hal generalisasi dan interpretabilitas. Studi ini mengusulkan kerangka kerja Stacking Ensemble Learning yang mengintegrasikan tiga model dasar—Logistic Regression (LR), Support Vector Machine (SVM) dengan kernel RBF, dan Random Forest (RF)—yang dikombinasikan dengan meta-learner untuk meningkatkan akurasi dan stabilitas prediksi depresi pada siswa. Eksperimen dilakukan pada dataset berskala besar yang mencakup 27.901 entri, dengan penerapan preprocessing, standardisasi, penyeimbangan kelas menggunakan SMOTE, dan validasi silang stratifikasi. Evaluasi performa menggunakan metrik Confusion Matrix, F1-Score, Recall, Precision, serta AUC-ROC Curve. Hasil menunjukkan bahwa model ansambel yang diusulkan mencapai akurasi 84%, AUC 0,911, dan rata-rata precision 0,905, yang secara konsisten melampaui performa model individual. Temuan ini menegaskan bahwa kombinasi antara model berbasis margin, non-linear, dan pohon keputusan mampu menghasilkan prediksi yang lebih andal dan dapat dijelaskan, sehingga potensial untuk diimplementasikan dalam sistem pemantauan kesehatan mental berbasis institusi pendidikan. Kata kunci: deteksi depresi, kesehatan mental siswa, stacking ensemble, machine learning.
Co-Authors Abda Abda Adela Fitri Afdhal Syafinur Afdhal Syafnur afrisawati, Afrisawati Ahmad Ariandi Andri Nata Andy Sapta Ariandi, Ahmad Arridha Zikra Syah Awanda Sari, Windi Chan, Zulfira Elisa Dalimunthe, Roni Dewi Maharani Edi Kurniawan Endra Saputra Fifi Safitri Fitri, Adela Gultom, Karyawaty Guntur Maha Putra Hanafi Asnan Ibnu Mardhotillah Indah Purnama Sari Jeperson Hutahaean Jhonson Efendi Hutagalung Karyawaty Gultom Laina Tussifah Lia Umbari Putri Lia, Lia Umbari Putri M Rizky Pratama Marpaung, Nasrun Masri Wahyuni Meri Ulfa Sari Muhammad Amin Muhammad Amin Nasrun Marpaung Nasution, Akmal Nur Adha Rini Nuriadi Manurung Putri, Lia Umbari Rahayu, Elly Rika Nofitri Risnawati Risnawati Risnawati Risnawati Rohminatin Rohminatin Rohminatin, Rohminatin Roni Dalimunthe sahren, sahren Santoso Santoso Sapta, Andy Satria Yudha Prayogi Siagian, Yessica Sitorus Pane, Siti Fatimah Sri Ayu Rizky Sri Maharani Sudarmin Sudarmin Sudarmin Sudarmin Sutan Hirmansyah Siregar Suwati Suwati Syartika Anggraini Titin, Tri Wanti USWATUN HASANAH Uswatun Hasanah William Ramdhan Zikra Syah , Arridha Zulfira Elisa Chan