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All Journal KALAMATIKA Jurnal Pendidikan Matematika SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan bit-Tech Jurnal Informatika Teknologi dan Sains (Jinteks) INTERNAL (Information System Journal)
Dwi Sulistya Kusumaningrum
Universitas Buana Perjuangan Karawang
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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PERBANDINGAN KEBIASAAN BERPIKIR DAN HASIL BELAJAR MATEMATIK ANTARA SISWA SANTRI DAN NON-SANTRI Santi Arum Puspita Lestari; Dwi Sulistya Kusumaningrum
KALAMATIKA Jurnal Pendidikan Matematika Vol 4 No 2 (2019): KALAMATIKA November 2019
Publisher : FKIP Universitas Muhammadiyah Prof. DR. HAMKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (424.722 KB) | DOI: 10.22236/KALAMATIKA.vol4no2.2019pp141-150

Abstract

This study aims to examine differences in mathematical habits of mind and mathematical learning outcomes between santri students and non-santri students. The design of this study is comparative research. The research data was collected by interviewing a mathematics teacher and spreading the scale of mathematical habits of mind. This research was conducted in two schools in Karawang precisely in Rawamerta. The schools used are private schools run by pesantren foundations for the sample of santri students and public schools as samples of non-santri students. From the results of data processing in the results obtained that there is no difference in mathematical habits of mind between santri students and non-santri students. In addition, there is no difference in mathematical learning outcomes between students of santri and non-santri. Meanwhile, there is an association between mathematical habits of mind and mathematical learning outcomes of students.
The Performance Comparison of Classification Algorithm in Order to Detecting Heart Disease Chepy Sonjaya; Anis Fitri Nur Masruriyah; Dwi Sulistya Kusumaningrum; Adi Rizky Pratama
INTERNAL (Information System Journal) Vol. 5 No. 2 (2022)
Publisher : Masoem University

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

Abstract

Heart disease in Indonesia, especially in the productive age, there is always an increase in the number of cases. The main cause of the increase in the number of heart patients is an unhealthy lifestyle and diet. The increase in patients with heart disease also has an impact on decreasing the standard of living. With this in mind, there is a need for research related to comparing classification methods on heart disease datasets. The dataset obtained is not balanced so that an oversampling technique is needed. The oversampling technique used is SMOTE. This research method uses Support Vector Machine (SVM) and Logistic Regression (LR). In order for this research method to be applied successfully, the data acquisition, data pre-processing and data transformation techniques are used to ensure accurate results. The model evaluation technique used is K-Fold Cross Validation. Based on the results of the analysis, it showed that the data partition using k-fold cross validation without oversampling gets the same accuracy value but the precision value is quite low. Conversely, if using the SMOTE technique, the accuracy value is as good as the precision value. The results of the SVM accuracy value get a value of 91.69%. LR is 91.76%. While the results of the SVM precision value of 57.81% and LR 54.82%. If using the SVM oversampling technique, the score is 75.79% and the LR is 75.84%. Meanwhile, the precision value obtained in SVM is 75.74%. At LR by 74.77%.
Penggunaan aplikasi matematika interaktif dalam proses pembelajaran bagi siswa SMP Santi Arum Puspita Lestari; Dwi Sulistya Kusumaningrum; Fitria Nurapriani; Rahmat Rahmat
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 8, No 2 (2024): June
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v8i2.23384

Abstract

AbstrakKemampuan untuk memanfaatkan teknologi digital menjadi sangat penting di era globalisasi dan kemajuan teknologi informasi saat ini, tidak hanya untuk individu tetapi juga untuk keberlangsungan berbagai sektor usaha dan industri. Menyadari pentingnya keterampilan digital, terutama di kalangan generasi muda, sekolah dipandang perlu mengintegrasikan pendidikan kewirausahaan dengan teknologi digital dalam kurikulum dan kegiatan ekstrakurikuler. Mitra dalam kegiatan pengabdian kepada masyarakat ini adalah SMA Kristen Palangka Raya. Terdapat beberapa permasalahan prioritas antara lain: kurangnya variasi kegiatan kewirausahaan yang dapat mengakomodir minat, bakat dan karakteristik siswa; kurangnya pengetahuan tentang pemanfaatan platform digital Canva untuk tujuan bisnis; kurangnya keterampilan tentang manajemen produksi suatu produk kewirausahaan berbasis digital. Berdasarkan permasalahan tersebut, kegiatan pengabdian ini bertujuan memberikan edukasi, berbagi pengetahuan kepada siswa dan guru tentang esensi kewirausahaan, pemanfaatan platform digital Canva untuk tujuan bisnis dan manajamen dasar kewirausahaan berbasis digital. Kegiatan ini melibatkan 23 orang siswa dan 4 orang guru sebagai peserta. Metode pelaksanaan melalui pelatihan dan pendampingan dengan teknik ceramah, tanya jawab, demonstrasi, tutorial dan praktik. Diperoleh hasil utama yaitu terjadinya peningkatan pengetahuan dan keterampilan siswa sebanyak 38,28%. Sedangkan hasil tambahannya adalah terciptanya prototype produk yang bernilai jual dari para siswa. Kata kunci: pelatihan; kewirausahaan digital; canva; manajemen; minat kewirausahaan. AbstrackThe ability to utilize digital technology has become crucial in the era of globalization and the advancement of information technology today, not only for individuals but also for the sustainability of various business and industrial sectors. Recognizing the importance of digital skills, especially among the younger generation, schools are seen as needing to integrate entrepreneurship education with digital technology into the curriculum and extracurricular activities. The partner in this community service activity is SMA Kristen Palangka Raya. There are several priority issues, including: the lack of variety in entrepreneurship activities that can accommodate the interests, talents, and characteristics of students; the lack of knowledge about utilizing the digital platform Canva for business purposes; and the lack of skills in managing the production of a digital-based entrepreneurial product. Based on these issues, this community service activity aims to provide education, share knowledge with students and teachers about the essence of entrepreneurship, the utilization of the digital platform Canva for business purposes, and basic management of digital-based entrepreneurship. This activity involves 23 students and 4 teachers as participants. The implementation methods include training and mentoring through lectures, Q&A sessions, demonstrations, tutorials, and practice. The main result obtained is an increase in students' knowledge and skills by 38.28%. Meanwhile, the additional result is the creation of prototype products with market value by the students. Keywords: training; digital entrepreneurship; canva; management; entrepreneurial interest.
Automatic Classification of Public Complaints Using Naive Bayes Rico Andrean Hardiansyah; Jamaludin Indra; Dwi Sulistya Kusumaningrum; Tohirin Al Mudzakir
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2992

Abstract

Public complaint services are essential for improving government service quality by providing a direct channel for citizens to report issues. In Karawang Regency, the Tanggap Karawang (TANGKAR) platform serves this function; however, the manual classification of complaints causes delays and potential misrouting, especially due to the highly imbalanced distribution of complaint categories. This study develops an automatic classification model for public complaints in eight categories economy, education, health, social, infrastructure, security, environment, and transportation by integrating Term Frequency–Inverse Document Frequency (TF–IDF), Multinomial Naive Bayes, and Synthetic Minority Oversampling Technique (SMOTE). This integration addresses domain-specific class imbalance challenges, combining the computational efficiency of Naive Bayes, the feature representation strength of TF–IDF, and the improved minority class recognition from SMOTE. A dataset of 800 complaint records from TANGKAR underwent preprocessing, including cleaning, case folding, normalization, tokenizing, stemming, and stopword removal. TF–IDF with unigram and bigram features was used for feature extraction, followed by classification under two scenarios: original unbalanced data and balanced data via SMOTE. Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix. The model achieved 85.09% accuracy without SMOTE and 83.40% with SMOTE, with notable improvement in detecting minority categories after balancing. Although overall accuracy slightly decreased, SMOTE enhanced equitable prediction across all categories. This approach advances current public complaint classification methods by adapting to the linguistic diversity and uneven category distribution in actual e-government data, supporting faster and more accurate decision-making in public complaint management systems.
PERBANDINGAN KINERJA RANDOM FOREST DAN XGBOOST UNTUK PREDIKSI KETERLAMBATAN PINJAMAN ONLINE Oktavia Nursyahbani; Sutan Faisal; Dwi Sulistya Kusumaningrum; Ayu Ratna Juwita
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 2 (2026): EDISI 28
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i2.7393

Abstract

Pertumbuhan industri teknologi finansial (fintech) di Indonesia membawa risiko peningkatan kredit bermasalah atau non-performing loan. Penelitian ini bertujuan membandingkan performa dua algoritma machine learning, yaitu Random Forest dan XGBoost, dalam memprediksi keterlambatan pembayaran pinjaman online menggunakan dataset Lending Club dari Kaggle yang terdiri dengan 1.048.575 baris data. Kontribusi ilmiah penelitian ini terletak pada analisis komparatif penggunaan Class Weight dan SMOTE pada dataset skala besar serta penerapan threshold tuning untuk optimasi model. Hasil penelitian ini menunjukkan bahwa XGBoost yang dioptimalkan melalui GridSearchCV dan Threshold Tuning menghasilkan akurasi 78,87%% dan ROC-AUC 0,7211. Namun secara analitis, rendahnya nilai Recall (7,21%) pada XGBoost menunjukkan sifat model yang sangat konservatif. Sebaliknya, Random Forest dengan Class Weight menghasilkan F1-Score tertinggi (0,3673) dan lebih sensitif mendeteksi kasus gagal bayar. Penelitian ini menyimpulkan bahwa meskipun XGBoost unggul dalam presisi, Class Weight lebih efektif daripada SMOTE dalam mempertahankan integritas distribusi data asli untuk identifikasi Risiko.
Co-Authors Adi Rizky Pratama Anis Fitri Nur Masruriyah Ayu Ratna Juwita Chepy Sonjaya Fitria Nurapriani Indra, Jamaludin Lestari, Santi Arum Puspita Mudzakir, Tohirin Al Oktavia Nursyahbani Rahmat Rahmat Rico Andrean Hardiansyah Sutan Faisal